## id Bank Year TA
## Min. : 1.00 AMRB.OQ: 9 Min. :2010 Min. :5.789e+08
## 1st Qu.: 37.75 FCCO.OQ: 9 1st Qu.:2012 1st Qu.:3.144e+09
## Median : 74.50 BOCH.OQ: 9 Median :2014 Median :6.760e+09
## Mean : 74.50 SHBI.OQ: 9 Mean :2014 Mean :7.744e+10
## 3rd Qu.:111.25 CVCY.OQ: 9 3rd Qu.:2016 3rd Qu.:1.820e+10
## Max. :148.00 PROV.OQ: 9 Max. :2018 Max. :2.620e+12
## (Other):1278
## RoE RoA LtD LoanstoTA
## Min. :-137.870 Min. :-6.1400 Min. :0.2480 Min. :0.2168
## 1st Qu.: 6.815 1st Qu.: 0.7850 1st Qu.:0.7626 1st Qu.:0.5997
## Median : 8.945 Median : 0.9918 Median :0.8789 Median :0.6797
## Mean : 8.436 Mean : 0.9498 Mean :0.8673 Mean :0.6595
## 3rd Qu.: 10.880 3rd Qu.: 1.1941 3rd Qu.:0.9599 3rd Qu.:0.7399
## Max. : 43.150 Max. : 5.7000 Max. :5.4337 Max. :0.9372
##
## NonIntInc_pct Efficiency LeverageRatio T1LeverageRatio
## Min. :-0.09472 Min. : 29.33 Min. :0.01677 Min. :0.0670
## 1st Qu.: 0.14594 1st Qu.: 57.32 1st Qu.:0.09790 1st Qu.:0.0869
## Median : 0.22604 Median : 63.17 Median :0.11154 Median :0.0947
## Mean : 0.22924 Mean : 63.44 Mean :0.11319 Mean :0.0968
## 3rd Qu.: 0.30118 3rd Qu.: 68.77 3rd Qu.:0.12587 3rd Qu.:0.1034
## Max. : 0.67983 Max. :172.27 Max. :0.21597 Max. :0.1870
## NA's :535
## T1_pct LoanLossProv_pct NonPerfLoans_pct LongTermDebt_pct
## Min. : 6.00 Min. :-0.0197099 Min. :0.00000 Min. :0.000000
## 1st Qu.:11.64 1st Qu.: 0.0009734 1st Qu.:0.00618 1st Qu.:0.007038
## Median :12.93 Median : 0.0022666 Median :0.01243 Median :0.017296
## Mean :13.58 Mean : 0.0045573 Mean :0.01934 Mean :0.030290
## 3rd Qu.:14.55 3rd Qu.: 0.0047699 3rd Qu.:0.02455 3rd Qu.:0.029517
## Max. :42.75 Max. : 0.0735433 Max. :0.18231 Max. :0.799814
## NA's :61 NA's :126
## Inflation GDP_pct FED_rate
## Min. :0.1186 Min. :0.8162 Min. :0.08917
## 1st Qu.:1.4648 1st Qu.:1.1385 1st Qu.:0.10750
## Median :1.6400 Median :1.5644 Median :0.14000
## Mean :1.7674 Mean :1.5214 Mean :0.44157
## 3rd Qu.:2.1301 3rd Qu.:1.7167 3rd Qu.:0.39500
## Max. :3.1568 Max. :2.2917 Max. :1.83167
##
## data_short %>% select(-1, -2, -3)
##
## 16 Variables 1332 Observations
## --------------------------------------------------------------------------------
## TA
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1317 1 7.744e+10 1.382e+11 1.267e+09 1.582e+09
## .25 .50 .75 .90 .95
## 3.144e+09 6.760e+09 1.820e+10 6.521e+10 2.060e+11
##
## lowest : 5.78940e+08 5.81518e+08 5.92753e+08 5.93887e+08 5.96389e+08
## highest: 2.42000e+12 2.49000e+12 2.53000e+12 2.57000e+12 2.62000e+12
## --------------------------------------------------------------------------------
## RoE
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1281 1 8.436 4.936 2.160 4.275
## .25 .50 .75 .90 .95
## 6.815 8.945 10.880 13.000 14.788
##
## lowest : -137.87000 -58.60000 -34.52250 -34.33186 -34.09000
## highest: 23.93000 24.80000 25.40000 40.60000 43.15000
## --------------------------------------------------------------------------------
## RoA
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1050 1 0.9498 0.489 0.2395 0.4925
## .25 .50 .75 .90 .95
## 0.7850 0.9918 1.1941 1.4100 1.6033
##
## lowest : -6.14000 -2.85000 -2.57669 -1.80760 -1.77500
## highest: 2.94000 3.09800 3.10500 3.17167 5.70000
## --------------------------------------------------------------------------------
## LtD
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1332 1 0.8673 0.193 0.5634 0.6486
## .25 .50 .75 .90 .95
## 0.7626 0.8789 0.9599 1.0313 1.1211
##
## lowest : 0.2480464 0.2667948 0.2875208 0.3377034 0.3851405
## highest: 1.5155728 1.5236227 2.1023991 3.1134590 5.4337110
## --------------------------------------------------------------------------------
## LoanstoTA
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1332 1 0.6595 0.1305 0.4194 0.4907
## .25 .50 .75 .90 .95
## 0.5997 0.6797 0.7399 0.7867 0.8196
##
## lowest : 0.2167902 0.2336244 0.2520854 0.2918288 0.2966595
## highest: 0.9147092 0.9148993 0.9171238 0.9317139 0.9371815
## --------------------------------------------------------------------------------
## NonIntInc_pct
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1332 1 0.2292 0.1286 0.05414 0.08317
## .25 .50 .75 .90 .95
## 0.14594 0.22604 0.30118 0.38163 0.42704
##
## lowest : -0.09472074 -0.03836141 -0.02688185 -0.02324207 -0.01025759
## highest: 0.63002153 0.64343266 0.64481168 0.66360437 0.67982548
## --------------------------------------------------------------------------------
## Efficiency
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1332 1 63.44 12.51 44.11 49.19
## .25 .50 .75 .90 .95
## 57.32 63.17 68.77 76.09 83.39
##
## lowest : 29.32553 31.66164 33.49141 33.64349 33.75433
## highest: 109.43046 123.75694 129.26516 152.02659 172.26959
## --------------------------------------------------------------------------------
## LeverageRatio
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1332 1 0.1132 0.02422 0.08244 0.08851
## .25 .50 .75 .90 .95
## 0.09790 0.11154 0.12587 0.13903 0.15344
##
## lowest : 0.01677276 0.03546389 0.03811750 0.03952972 0.04298529
## highest: 0.19914474 0.20480579 0.20657599 0.20846264 0.21597261
## --------------------------------------------------------------------------------
## T1LeverageRatio
## n missing distinct Info Mean Gmd .05 .10
## 797 535 797 1 0.09683 0.016 0.07632 0.08052
## .25 .50 .75 .90 .95
## 0.08693 0.09469 0.10344 0.11496 0.12264
##
## lowest : 0.06696443 0.06773576 0.06779661 0.06859491 0.06859504
## highest: 0.16263428 0.16848997 0.17113544 0.17938062 0.18704062
## --------------------------------------------------------------------------------
## T1_pct
## n missing distinct Info Mean Gmd .05 .10
## 1271 61 650 1 13.58 3.129 10.21 10.65
## .25 .50 .75 .90 .95
## 11.64 12.93 14.55 17.33 19.05
##
## lowest : 6.00 6.09 6.21 6.81 6.90, highest: 31.75 33.03 36.57 38.47 42.75
## --------------------------------------------------------------------------------
## LoanLossProv_pct
## n missing distinct Info Mean Gmd .05
## 1332 0 1308 1 0.004557 0.006311 -0.0010828
## .10 .25 .50 .75 .90 .95
## 0.0000000 0.0009734 0.0022666 0.0047699 0.0121358 0.0187905
##
## lowest : -0.019709879 -0.008568730 -0.007767283 -0.007744641 -0.006836029
## highest: 0.060671818 0.061076296 0.067616232 0.068633400 0.073543267
## --------------------------------------------------------------------------------
## NonPerfLoans_pct
## n missing distinct Info Mean Gmd .05 .10
## 1206 126 1206 1 0.01934 0.01935 0.001918 0.003252
## .25 .50 .75 .90 .95
## 0.006184 0.012429 0.024548 0.042931 0.061888
##
## lowest : 0.000000e+00 4.584461e-05 5.772795e-05 8.362473e-05 1.594983e-04
## highest: 1.413719e-01 1.440444e-01 1.443116e-01 1.633339e-01 1.823105e-01
## --------------------------------------------------------------------------------
## LongTermDebt_pct
## n missing distinct Info Mean Gmd .05 .10
## 1332 0 1147 0.997 0.03029 0.03681 0.000000 0.000000
## .25 .50 .75 .90 .95
## 0.007038 0.017296 0.029517 0.082259 0.115229
##
## lowest : 0.000000e+00 3.253073e-05 5.163573e-05 7.948689e-05 2.331196e-04
## highest: 3.968581e-01 4.170735e-01 5.756029e-01 7.231403e-01 7.998144e-01
## --------------------------------------------------------------------------------
## Inflation
## n missing distinct Info Mean Gmd
## 1332 0 9 0.988 1.767 0.8635
##
## lowest : 0.1186271 1.2615832 1.4648327 1.6222230 1.6400434
## highest: 1.6400434 2.0693373 2.1301100 2.4425833 3.1568416
##
## Value 0.1186271 1.2615832 1.4648327 1.6222230 1.6400434 2.0693373
## Frequency 148 148 148 148 148 148
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111
##
## Value 2.1301100 2.4425833 3.1568416
## Frequency 148 148 148
## Proportion 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## GDP_pct
## n missing distinct Info Mean Gmd
## 1332 0 9 0.988 1.521 0.55
##
## lowest : 0.8162317 0.8351279 1.1384973 1.5021706 1.5644444
## highest: 1.5644444 1.7026342 1.7167482 2.1251228 2.2917295
##
## Value 0.8162317 0.8351279 1.1384973 1.5021706 1.5644444 1.7026342
## Frequency 148 148 148 148 148 148
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111
##
## Value 1.7167482 2.1251228 2.2917295
## Frequency 148 148 148
## Proportion 0.111 0.111 0.111
## --------------------------------------------------------------------------------
## FED_rate
## n missing distinct Info Mean Gmd
## 1332 0 9 0.988 0.4416 0.5084
##
## lowest : 0.08916667 0.10166667 0.10750000 0.13250000 0.14000000
## highest: 0.14000000 0.17500000 0.39500000 1.00166667 1.83166667
##
## Value 0.08916667 0.10166667 0.10750000 0.13250000 0.14000000 0.17500000
## Frequency 148 148 148 148 148 148
## Proportion 0.111 0.111 0.111 0.111 0.111 0.111
##
## Value 0.39500000 1.00166667 1.83166667
## Frequency 148 148 148
## Proportion 0.111 0.111 0.111
## --------------------------------------------------------------------------------
##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sun, Apr 12, 2020 - 11:27:30
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}}lccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## Statistic & \multicolumn{1}{c}{N} & \multicolumn{1}{c}{Mean} & \multicolumn{1}{c}{St. Dev.} & \multicolumn{1}{c}{Min} & \multicolumn{1}{c}{Pctl(25)} & \multicolumn{1}{c}{Pctl(75)} & \multicolumn{1}{c}{Max} \\
## \hline \\[-1.8ex]
## id & 1,332 & 74.500 & 42.739 & 1 & 37.8 & 111.2 & 148 \\
## Year & 1,332 & 2,014.000 & 2.583 & 2,010 & 2,012 & 2,016 & 2,018 \\
## TA & 1,332 & 22.910 & 1.583 & 20.177 & 21.869 & 23.625 & 28.594 \\
## RoE & 1,332 & 8.436 & 6.685 & $-$137.870 & 6.815 & 10.880 & 43.150 \\
## RoA & 1,332 & 0.950 & 0.541 & $-$6.140 & 0.785 & 1.194 & 5.700 \\
## LtD & 1,332 & 0.867 & 0.220 & 0.248 & 0.763 & 0.960 & 5.434 \\
## LoanstoTA & 1,332 & 0.660 & 0.119 & 0.217 & 0.600 & 0.740 & 0.937 \\
## NonIntInc\_pct & 1,332 & 0.229 & 0.114 & $-$0.095 & 0.146 & 0.301 & 0.680 \\
## Efficiency & 1,332 & 63.439 & 11.995 & 29.326 & 57.323 & 68.771 & 172.270 \\
## LeverageRatio & 1,332 & 0.113 & 0.022 & 0.017 & 0.098 & 0.126 & 0.216 \\
## T1LeverageRatio & 797 & 0.097 & 0.015 & 0.067 & 0.087 & 0.103 & 0.187 \\
## T1\_pct & 1,271 & 13.576 & 3.222 & 6.000 & 11.645 & 14.550 & 42.750 \\
## LoanLossProv\_pct & 1,332 & 0.005 & 0.008 & $-$0.020 & 0.001 & 0.005 & 0.074 \\
## NonPerfLoans\_pct & 1,206 & 0.019 & 0.021 & 0.000 & 0.006 & 0.025 & 0.182 \\
## LongTermDebt\_pct & 1,332 & 0.030 & 0.051 & 0.000 & 0.007 & 0.030 & 0.800 \\
## Inflation & 1,332 & 1.767 & 0.797 & 0.119 & 1.465 & 2.130 & 3.157 \\
## GDP\_pct & 1,332 & 1.521 & 0.488 & 0.816 & 1.138 & 1.717 & 2.292 \\
## FED\_rate & 1,332 & 0.442 & 0.564 & 0.089 & 0.108 & 0.395 & 1.832 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}
data_stata_win_summary <- data_stata_win_reg %>%
gather(key = "Variable", value = "Value", TA:LeverageRatio, T1_pct:GDP_pct) %>%
filter(is.na(Value) != TRUE) %>%
group_by(Variable) %>%
summarise(N = n(),
Mean = round(mean(Value, na.rm = TRUE),2),
'St. Dev.' = round(sd(Value, na.rm = TRUE),2),
Min = round(min(Value, na.rm = TRUE),2),
'Pctl(25)' = round(quantile(Value, 0.25, na.rm = TRUE),2),
Median = round(quantile(Value, 0.50, na.rm = TRUE),2),
'Pctl(75)' = round(quantile(Value, 0.75, na.rm = TRUE),2),
Max = round(max(Value, na.rm = TRUE),2))
rownames(data_stata_win_summary) <- data_stata_win_summary$Variable## Warning: Setting row names on a tibble is deprecated.
data_stata_win_summary <- data_stata_win_summary %>%
select(-Variable)
stargazer(data_stata_win_summary, summary = FALSE)##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sun, Apr 12, 2020 - 11:27:30
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}} ccccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## & N & Mean & St. Dev. & Min & Pctl(25) & Median & Pctl(75) & Max \\
## \hline \\[-1.8ex]
## Efficiency & 1332 & 63.31 & 11.25 & 34.44 & 57.32 & 63.17 & 68.77 & 102.36 \\
## GDP\_pct & 1332 & 1.52 & 0.49 & 0.82 & 1.14 & 1.56 & 1.72 & 2.29 \\
## Inflation & 1332 & 1.77 & 0.8 & 0.12 & 1.46 & 1.64 & 2.13 & 3.16 \\
## LeverageRatio & 1332 & 11.32 & 2.16 & 6.26 & 9.79 & 11.15 & 12.59 & 19.06 \\
## LoanLossProv\_pct & 1332 & 0.45 & 0.74 & -0.55 & 0.1 & 0.23 & 0.48 & 5.24 \\
## LoanstoTA & 1332 & 65.97 & 11.78 & 31.1 & 59.97 & 67.97 & 73.99 & 91.33 \\
## LongTermDebt\_pct & 1332 & 2.9 & 3.87 & 0 & 0.7 & 1.73 & 2.95 & 23.68 \\
## LtD & 1332 & 86.3 & 16.72 & 41.71 & 76.26 & 87.89 & 95.99 & 147.67 \\
## NonIntInc\_pct & 1332 & 22.91 & 11.3 & 0.9 & 14.59 & 22.6 & 30.12 & 57.81 \\
## NonPerfLoans\_pct & 1206 & 1.92 & 2.01 & 0.04 & 0.62 & 1.24 & 2.45 & 12.38 \\
## RoA & 1332 & 0.95 & 0.47 & -1.61 & 0.78 & 0.99 & 1.19 & 2.21 \\
## RoE & 1332 & 8.55 & 4.74 & -20.99 & 6.81 & 8.95 & 10.88 & 22.64 \\
## T1\_pct & 1271 & 13.56 & 3.02 & 8.6 & 11.64 & 12.93 & 14.55 & 29.68 \\
## TA & 1332 & 22.91 & 1.58 & 20.18 & 21.87 & 22.63 & 23.62 & 28.59 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}
With est_RW
data_stata_win_summary <- data_stata_win_reg %>%
gather(key = "Variable", value = "Value", TA:LeverageRatio, T1_pct:GDP_pct, est_RW) %>%
filter(is.na(Value) != TRUE) %>%
group_by(Variable) %>%
summarise(N = n(),
Mean = round(mean(Value, na.rm = TRUE),2),
'St. Dev.' = round(sd(Value, na.rm = TRUE),2),
Min = round(min(Value, na.rm = TRUE),2),
'Pctl(25)' = round(quantile(Value, 0.25, na.rm = TRUE),2),
Median = round(quantile(Value, 0.50, na.rm = TRUE),2),
'Pctl(75)' = round(quantile(Value, 0.75, na.rm = TRUE),2),
Max = round(max(Value, na.rm = TRUE),2))
rownames(data_stata_win_summary) <- data_stata_win_summary$Variable## Warning: Setting row names on a tibble is deprecated.
data_stata_win_summary <- data_stata_win_summary %>%
select(-Variable)
stargazer(data_stata_win_summary, summary = FALSE)##
## % Table created by stargazer v.5.2.2 by Marek Hlavac, Harvard University. E-mail: hlavac at fas.harvard.edu
## % Date and time: Sun, Apr 12, 2020 - 11:27:30
## \begin{table}[!htbp] \centering
## \caption{}
## \label{}
## \begin{tabular}{@{\extracolsep{5pt}} ccccccccc}
## \\[-1.8ex]\hline
## \hline \\[-1.8ex]
## & N & Mean & St. Dev. & Min & Pctl(25) & Median & Pctl(75) & Max \\
## \hline \\[-1.8ex]
## Efficiency & 1332 & 63.31 & 11.25 & 34.44 & 57.32 & 63.17 & 68.77 & 102.36 \\
## est\_RW & 1271 & 72.28 & 12.94 & 30.68 & 63.84 & 72.79 & 80.51 & 123.51 \\
## GDP\_pct & 1332 & 1.52 & 0.49 & 0.82 & 1.14 & 1.56 & 1.72 & 2.29 \\
## Inflation & 1332 & 1.77 & 0.8 & 0.12 & 1.46 & 1.64 & 2.13 & 3.16 \\
## LeverageRatio & 1332 & 11.32 & 2.16 & 6.26 & 9.79 & 11.15 & 12.59 & 19.06 \\
## LoanLossProv\_pct & 1332 & 0.45 & 0.74 & -0.55 & 0.1 & 0.23 & 0.48 & 5.24 \\
## LoanstoTA & 1332 & 65.97 & 11.78 & 31.1 & 59.97 & 67.97 & 73.99 & 91.33 \\
## LongTermDebt\_pct & 1332 & 2.9 & 3.87 & 0 & 0.7 & 1.73 & 2.95 & 23.68 \\
## LtD & 1332 & 86.3 & 16.72 & 41.71 & 76.26 & 87.89 & 95.99 & 147.67 \\
## NonIntInc\_pct & 1332 & 22.91 & 11.3 & 0.9 & 14.59 & 22.6 & 30.12 & 57.81 \\
## NonPerfLoans\_pct & 1206 & 1.92 & 2.01 & 0.04 & 0.62 & 1.24 & 2.45 & 12.38 \\
## RoA & 1332 & 0.95 & 0.47 & -1.61 & 0.78 & 0.99 & 1.19 & 2.21 \\
## RoE & 1332 & 8.55 & 4.74 & -20.99 & 6.81 & 8.95 & 10.88 & 22.64 \\
## T1\_pct & 1271 & 13.56 & 3.02 & 8.6 & 11.64 & 12.93 & 14.55 & 29.68 \\
## TA & 1332 & 22.91 & 1.58 & 20.18 & 21.87 & 22.63 & 23.62 & 28.59 \\
## \hline \\[-1.8ex]
## \end{tabular}
## \end{table}
Just 2018
Just 2018
cor_stata_win_diag <- cor_stata_win
diag(cor_stata_win_diag) = NA
corrplot(cor_stata_win_diag, order = "hclust",
tl.col = "black", tl.srt = 45,
method = "color", na.label = "-",
p.mat = rcor_stata_win$P,
insig = "label_sig", sig.level = c(.001, .01, .05), pch.cex = .8, pch.col = "black")cor_stata_win_year_DF %>%
ggplot(aes(x = Year, y = RoE, colour = Variable, group = Variable)) +
geom_line(size = 1) +
scale_x_continuous(expand = c(0.15, 0.15), breaks = seq(2010, 2018, 2)
) +
geom_dl(aes(label = Variable), method = list(dl.combine("first.points", "last.points"), cex = 0.6)) +
labs(y = "Correlation with RoE")cor_stata_win_year_DF %>%
ggplot(aes(x = Year, y = RoA, colour = Variable, group = Variable)) +
geom_line(size = 1) +
scale_x_continuous(expand = c(0.15, 0.15), breaks = seq(2010, 2018, 2)
) +
geom_dl(aes(label = Variable), method = list(dl.combine("first.points", "last.points"), cex = 0.6)) +
labs(y = "Correlation with RoE")cor_stata_win_year_DF %>%
ggplot(aes(x = Year, y = TA, colour = Variable, group = Variable)) +
geom_line(size = 1) +
scale_x_continuous(expand = c(0.15, 0.15), breaks = seq(2010, 2018, 2)
) +
geom_dl(aes(label = Variable), method = list(dl.combine("first.points", "last.points"), cex = 0.6)) +
labs(y = "Correlation with RoE")cor_stata_win_year_DF %>%
ggplot(aes(x = Year, y = T1_pct, colour = Variable, group = Variable)) +
geom_line(size = 1) +
scale_x_continuous(expand = c(0.15, 0.15), breaks = seq(2010, 2018, 2)
) +
geom_dl(aes(label = Variable), method = list(dl.combine("first.points", "last.points"), cex = 0.6)) +
labs(y = "Correlation with RoE")cor_stata_win_year_DF %>%
ggplot(aes(x = Year, y = LoanLossProv_pct, colour = Variable, group = Variable)) +
geom_line(size = 1) +
scale_x_continuous(expand = c(0.15, 0.15), breaks = seq(2010, 2018, 2)
) +
geom_dl(aes(label = Variable), method = list(dl.combine("first.points", "last.points"), cex = 0.6)) +
labs(y = "Correlation with RoE")data_stata_z %>%
gather(key = "variable", value = "value", TA:LeverageRatio, T1_pct:GDP_pct, est_RW) %>%
group_by(Year, variable) %>%
summarise(value = mean(value, na.rm = TRUE)) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
ggplot(aes(x = Year, y = value, colour = variable, group = variable)) +
geom_line(size = 0.9) +
scale_x_continuous(expand = c(0.15, 0.15), breaks = seq(2010, 2018, 2)) +
geom_dl(aes(label = variable), method = list(dl.combine("first.points", "last.points"), cex = 0.6)) +
labs(y = "Z-Score")data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100) %>%
gather(key = "variable", value = "value", TA:LeverageRatio, T1_pct:GDP_pct) %>%
group_by(Year, variable) %>%
summarise(mean = mean(value, na.rm = TRUE)) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(10, 9, 12, 1, 7, 2, 11, 3, 8, 6, 4, 5)],
labels = c("RoE", "RoA", "ln(TA)", "Efficiency", "Non-interest income", "Leverage Ratio", "T1 (% RWAs)", "Loan Loss Provisions", "Non-performing Loans", "Loans to Deposits", "Loans to Total Assets", "Long-term Debt"))) %>%
ggplot(aes(x = Year, y = mean, colour = variable, group = variable)) +
geom_line(size = 0.9) +
scale_x_continuous(breaks = seq(2010, 2018, 4)) +
facet_wrap(~ variable, scales = "free") +
theme(legend.position = "none",
axis.title.x = element_blank()) +
labs(title = "Change in mean value for each variable over time", y = "mean
")data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100) %>%
gather(key = "variable", value = "value", TA:LeverageRatio, T1_pct:GDP_pct) %>%
group_by(Year, variable) %>%
summarise(median = median(value, na.rm = TRUE),
first = quantile(value, 0.25, na.rm = TRUE),
third = quantile(value, 0.75, na.rm = TRUE)) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(10, 9, 12, 1, 7, 2, 11, 3, 8, 6, 4, 5)],
labels = c("RoE", "RoA", "ln(TA)", "Efficiency", "Non-interest income", "Leverage Ratio", "T1 (% RWAs)", "Loan Loss Provisions", "Non-performing Loans", "Loans to Deposits", "Loans to Total Assets", "Long-term Debt"))) %>%
ggplot(aes(x = Year, y = median, colour = variable, group = variable)) +
geom_line(size = 0.9) +
geom_line(aes(y = first), alpha = 0.3, size = 0.9) +
geom_line(aes(y = third), alpha = 0.3, size = 0.9) +
scale_x_continuous(breaks = seq(2010, 2018, 4)) +
facet_wrap(~ variable, scales = "free") +
theme(legend.position = "none",
axis.title.x = element_blank()) +
labs(title = "Change in median and IQR for each variable over time", y = "Median and IQR
")data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100) %>%
gather(key = "variable", value = "value", TA:LeverageRatio, T1_pct:GDP_pct) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(10, 9, 12, 1, 7, 2, 11, 3, 8, 6, 4, 5)],
labels = c("RoE", "RoA", "ln(TA)", "Efficiency", "Non-interest income", "Leverage Ratio", "T1 (% RWAs)", "Loan Loss Provisions", "Non-performing Loans", "Loans to Deposits", "Loans to Total Assets", "Long-term Debt"))) %>%
ggplot(aes(x = Year, y = value, fill = variable, group = Year)) +
geom_boxplot(outlier.colour = "grey") +
scale_x_continuous(breaks = seq(2010, 2018, 4)) +
facet_wrap(~ variable, scales = "free") +
theme(legend.position = "none",
axis.title.x = element_blank(),
axis.title.y = element_blank())## Warning: Removed 187 rows containing non-finite values (stat_boxplot).
data %>%
ggplot(aes(x = Year, y = TA, group = Year)) +
geom_boxplot() +
scale_y_log10() +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = Year, y = LoanLossProv_pct, group = Year)) +
geom_boxplot() +
scale_y_continuous(labels = percent_format()) +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = Year, y = NonPerfLoans_pct, group = Year)) +
geom_boxplot() +
scale_y_continuous(labels = percent_format()) +
scale_x_continuous(breaks = seq(2010, 2018, 1))## Warning: Removed 126 rows containing non-finite values (stat_boxplot).
data %>%
ggplot(aes(x = Year, y = Efficiency, group = Year)) +
geom_boxplot() +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = Year, y = T1_pct, group = Year)) +
geom_boxplot() +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = Year, y = LeverageRatio, group = Year)) +
geom_boxplot() +
scale_y_continuous(labels = percent_format()) +
scale_x_continuous(breaks = seq(2010, 2018, 1))data_stata %>%
ggplot(aes(x = Year, y = est_RW, group = Year)) +
geom_boxplot() +
scale_x_continuous(breaks = seq(2010, 2018, 1))## Warning: Removed 61 rows containing non-finite values (stat_boxplot).
data %>%
ggplot(aes(x = Year, y = NonIntInc_pct, group = Year)) +
geom_boxplot() +
scale_y_continuous(labels = percent_format()) +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = Year, y = LtD, group = Year)) +
geom_boxplot() +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = Year, y = LongTermDebt_pct, group = Year)) +
geom_boxplot() +
scale_y_continuous(labels = percent_format()) +
scale_x_continuous(breaks = seq(2010, 2018, 1))data %>%
ggplot(aes(x = RoE)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.RoE = mean(RoE)), aes(xintercept = mean.RoE), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.RoE = quantile(RoE, 0.25)), aes(xintercept = st.RoE), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.RoE = quantile(RoE, 0.75)), aes(xintercept = rd.RoE), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = RoA)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.RoA = mean(RoA)), aes(xintercept = mean.RoA), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.RoA = quantile(RoA, 0.25)), aes(xintercept = st.RoA), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.RoA = quantile(RoA, 0.75)), aes(xintercept = rd.RoA), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = TA)) +
geom_histogram(bins = 75) +
scale_x_log10() +
facet_wrap(~ Year) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.TA = mean(TA, na.rm = TRUE)), aes(xintercept = mean.TA), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.TA = quantile(TA, 0.25, na.rm = TRUE)), aes(xintercept = st.TA), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.TA = quantile(TA, 0.75, na.rm = TRUE)), aes(xintercept = rd.TA), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = LoanLossProv_pct)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
scale_x_continuous(labels = percent_format()) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.LoanLossProv_pct = mean(LoanLossProv_pct, na.rm = TRUE)), aes(xintercept = mean.LoanLossProv_pct), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.LoanLossProv_pct = quantile(LoanLossProv_pct, 0.25, na.rm = TRUE)), aes(xintercept = st.LoanLossProv_pct), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.LoanLossProv_pct = quantile(LoanLossProv_pct, 0.75, na.rm = TRUE)), aes(xintercept = rd.LoanLossProv_pct), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = NonPerfLoans_pct)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
scale_x_continuous(labels = percent_format()) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.LoanLossProv_pct = mean(LoanLossProv_pct, na.rm = TRUE)), aes(xintercept = mean.LoanLossProv_pct), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.LoanLossProv_pct = quantile(LoanLossProv_pct, 0.25, na.rm = TRUE)), aes(xintercept = st.LoanLossProv_pct), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.LoanLossProv_pct = quantile(LoanLossProv_pct, 0.75, na.rm = TRUE)), aes(xintercept = rd.LoanLossProv_pct), linetype = "dashed", col = "orange")## Warning: Removed 126 rows containing non-finite values (stat_bin).
data %>%
ggplot(aes(x = Efficiency)) +
geom_histogram(bins = 75) +
xlim(0,180) +
facet_wrap(~ Year) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.Efficiency = mean(Efficiency)), aes(xintercept = mean.Efficiency), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.Efficiency = quantile(Efficiency, 0.25)), aes(xintercept = st.Efficiency), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.Efficiency = quantile(Efficiency, 0.75)), aes(xintercept = rd.Efficiency), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = T1_pct)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.T1_pct = mean(T1_pct, na.rm = TRUE)), aes(xintercept = mean.T1_pct), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.T1_pct = quantile(T1_pct, 0.25, na.rm = TRUE)), aes(xintercept = st.T1_pct), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.T1_pct = quantile(T1_pct, 0.75, na.rm = TRUE)), aes(xintercept = rd.T1_pct), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = LeverageRatio)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
scale_x_continuous(labels = percent_format()) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.LeverageRatio = mean(LeverageRatio, na.rm = TRUE)), aes(xintercept = mean.LeverageRatio), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.LeverageRatio = quantile(LeverageRatio, 0.25, na.rm = TRUE)), aes(xintercept = st.LeverageRatio), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.LeverageRatio = quantile(LeverageRatio, 0.75, na.rm = TRUE)), aes(xintercept = rd.LeverageRatio), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = NonIntInc_pct)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
scale_x_continuous(labels = percent_format()) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.NonIntInc_pct = mean(NonIntInc_pct, na.rm = TRUE)), aes(xintercept = mean.NonIntInc_pct), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.NonIntInc_pct = quantile(NonIntInc_pct, 0.25, na.rm = TRUE)), aes(xintercept = st.NonIntInc_pct), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.NonIntInc_pct = quantile(NonIntInc_pct, 0.75, na.rm = TRUE)), aes(xintercept = rd.NonIntInc_pct), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = LtD)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
scale_x_continuous(labels = percent_format()) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.LtD = mean(LtD, na.rm = TRUE)), aes(xintercept = mean.LtD), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.LtD = quantile(LtD, 0.25, na.rm = TRUE)), aes(xintercept = st.LtD), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.LtD = quantile(LtD, 0.75, na.rm = TRUE)), aes(xintercept = rd.LtD), linetype = "dashed", col = "orange")data %>%
ggplot(aes(x = LongTermDebt_pct)) +
geom_histogram(bins = 75) +
facet_wrap(~ Year) +
scale_x_continuous(labels = percent_format()) +
geom_vline(data = data %>% group_by(Year) %>% summarise(mean.LongTermDebt_pct = mean(LongTermDebt_pct, na.rm = TRUE)), aes(xintercept = mean.LongTermDebt_pct), linetype = "dashed", col = "red") +
geom_vline(data = data %>% group_by(Year) %>% summarise(st.LongTermDebt_pct = quantile(LongTermDebt_pct, 0.25, na.rm = TRUE)), aes(xintercept = st.LongTermDebt_pct), linetype = "dashed", col = "orange") +
geom_vline(data = data %>% group_by(Year) %>% summarise(rd.LongTermDebt_pct = quantile(LongTermDebt_pct, 0.75, na.rm = TRUE)), aes(xintercept = rd.LongTermDebt_pct), linetype = "dashed", col = "orange")residual %>%
ggplot(aes(x = r)) +
geom_histogram(bins = 75) +
scale_x_continuous(breaks = seq(-100, 100, 10))## Warning: Removed 61 rows containing non-finite values (stat_bin).
data_stata_z_gather %>%
filter(variable != "Inflation" & variable != "GDP_pct" & variable != "T1LeverageRatio") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(11, 10, 13, 3, 12, 4, 9, 7, 5, 6, 8, 1, 2)])) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_top_50))) +
geom_boxplot(outlier.colour = NA) +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5),
axis.title.x = element_blank()) +
labs(y = "z-score", fill = "RoE in top 50%")## Warning: Removed 248 rows containing non-finite values (stat_boxplot).
data_stata_z_gather %>%
filter(variable != "Inflation" & variable != "GDP_pct" & variable != "T1LeverageRatio") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(11, 10, 13, 3, 12, 4, 9, 7, 5, 6, 8, 1, 2)])) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_quartiles))) +
geom_boxplot(outlier.colour = NA) +
theme(axis.text.x = element_text(angle = 45, vjust = 0.9, hjust = 0.9),
axis.title.x = element_blank()) +
scale_fill_discrete(name = "RoE by quartiles", labels = c("1st quartile", "2nd quartile", "3rd quartile", "4th quartile")) +
scale_x_discrete(labels=c("TA" = "ln(TA)", "LeverageRatio" = "Leverage Ratio", "T1_pct" = "T1 (% RWAs)",
"LoanLossProv_pct" = "Loan Loss Provisions", "NonPerfLoans_pct" = "Non-performing Loans", "LtD" = "Loans to Deposits", "LoanstoTA" = "Loans to Total Assets", "LongTermDebt_pct" = "Long-term Debt", "NonIntInc_pct" = "Non-interest income", "est_RW" = "average risk-weight")) +
labs(y = "z-score", fill = "RoE by quartiles")## Warning: Removed 248 rows containing non-finite values (stat_boxplot).
Z-score
data_stata_z_gather %>%
filter(variable != "Inflation" & variable != "GDP_pct" & variable != "T1LeverageRatio" & variable != "est_RW") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(10, 9, 12, 1, 7, 2, 11, 3, 8, 6, 4, 5)],
labels = c("RoE", "RoA", "ln(TA)", "Efficiency", "Non-interest income", "Leverage Ratio", "T1 (% RWAs)", "Loan Loss Provisions", "Non-performing Loans", "Loans to Deposits", "Loans to Total Assets", "Long-term Debt"))) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_quartiles))) +
geom_boxplot(outlier.colour = "grey") +
scale_fill_discrete(name = "RoE by quartiles", labels = c("1st quartile", "2nd quartile", "3rd quartile", "4th quartile")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank()) +
facet_wrap(~ variable, scales = "free_x") +
labs(y = "z-score", fill = "RoE by quartiles")## Warning: Removed 187 rows containing non-finite values (stat_boxplot).
No need for z-score with scale free :)
data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100,
RoE_1 = if_else(RoE <= quantile(RoE, 0.25), 1, 0),
RoE_2 = if_else(RoE > quantile(RoE, 0.25) & RoE <= quantile(RoE, 0.50), 1, 0),
RoE_3 = if_else(RoE > quantile(RoE, 0.50) & RoE <= quantile(RoE, 0.75), 1, 0),
RoE_4 = if_else(RoE > quantile(RoE, 0.75) & RoE <= quantile(RoE, 1), 1, 0),
RoE_quartiles = if_else(RoE <= quantile(RoE, 0.25), 1, if_else(RoE <= quantile(RoE, 0.50), 2, if_else(RoE <= quantile(RoE, 0.75), 3, if_else(RoE <= quantile(RoE, 1), 4, 0)))),
RoE_top_10 = if_else(RoE > quantile(RoE, 0.90), 1, 0),
RoE_bottom_10 = if_else(RoE < quantile(RoE, 0.1), 1, 0),
RoE_top_50 = if_else(RoE > quantile(RoE, 0.5), 1, 0)) %>%
gather(key = "variable", value = "value", TA:LeverageRatio, T1_pct:GDP_pct) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(10, 9, 12, 1, 7, 2, 11, 3, 8, 6, 4, 5)],
labels = c("RoE", "RoA", "ln(TA)", "Efficiency", "Non-interest income", "Leverage Ratio", "T1 (% RWAs)", "Loan Loss Provisions", "Non-performing Loans", "Loans to Deposits", "Loans to Total Assets", "Long-term Debt"))) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_quartiles))) +
geom_boxplot(outlier.colour = "grey") +
scale_fill_discrete(name = "RoE by quartiles", labels = c("1st quartile", "2nd quartile", "3rd quartile", "4th quartile")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank()) +
facet_wrap(~ variable, scales = "free")## Warning: Removed 187 rows containing non-finite values (stat_boxplot).
data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100,
RoA_1 = if_else(RoA <= quantile(RoA, 0.25), 1, 0),
RoA_2 = if_else(RoA > quantile(RoA, 0.25) & RoA <= quantile(RoA, 0.50), 1, 0),
RoA_3 = if_else(RoA > quantile(RoA, 0.50) & RoA <= quantile(RoA, 0.75), 1, 0),
RoA_4 = if_else(RoA > quantile(RoA, 0.75) & RoA <= quantile(RoA, 1), 1, 0),
RoA_quartiles = if_else(RoA <= quantile(RoA, 0.25), 1, if_else(RoA <= quantile(RoA, 0.50), 2, if_else(RoA <= quantile(RoA, 0.75), 3, if_else(RoA <= quantile(RoA, 1), 4, 0)))),
RoA_top_10 = if_else(RoA > quantile(RoA, 0.90), 1, 0),
RoA_bottom_10 = if_else(RoA < quantile(RoA, 0.1), 1, 0),
RoA_top_50 = if_else(RoA > quantile(RoA, 0.5), 1, 0)) %>%
gather(key = "variable", value = "value", TA:LeverageRatio, T1_pct:GDP_pct) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(9, 10, 12, 1, 7, 2, 11, 3, 8, 6, 4, 5)],
labels = c("RoA", "RoE", "ln(TA)", "Efficiency", "Non-interest income", "Leverage Ratio", "T1 (% RWAs)", "Loan Loss Provisions", "Non-performing Loans", "Loans to Deposits", "Loans to Total Assets", "Long-term Debt"))) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoA_quartiles))) +
geom_boxplot(outlier.colour = "grey") +
scale_fill_discrete(name = "RoA by quartiles", labels = c("1st quartile", "2nd quartile", "3rd quartile", "4th quartile")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank()) +
facet_wrap(~ variable, scales = "free")## Warning: Removed 187 rows containing non-finite values (stat_boxplot).
RoA_RW <- data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100,
RoA_1 = if_else(RoA <= quantile(RoA, 0.25), 1, 0),
RoA_2 = if_else(RoA > quantile(RoA, 0.25) & RoA <= quantile(RoA, 0.50), 1, 0),
RoA_3 = if_else(RoA > quantile(RoA, 0.50) & RoA <= quantile(RoA, 0.75), 1, 0),
RoA_4 = if_else(RoA > quantile(RoA, 0.75) & RoA <= quantile(RoA, 1), 1, 0),
RoA_quartiles = if_else(RoA <= quantile(RoA, 0.25), 1, if_else(RoA <= quantile(RoA, 0.50), 2, if_else(RoA <= quantile(RoA, 0.75), 3, if_else(RoA <= quantile(RoA, 1), 4, 0)))),
RoA_top_10 = if_else(RoA > quantile(RoA, 0.90), 1, 0),
RoA_bottom_10 = if_else(RoA < quantile(RoA, 0.1), 1, 0),
RoA_top_50 = if_else(RoA > quantile(RoA, 0.5), 1, 0)) %>%
gather(key = "variable", value = "value", est_RW) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
ggplot(aes(y = value, x = variable, fill = factor(RoA_quartiles))) +
geom_boxplot(outlier.colour = "grey") +
scale_fill_discrete(name = "RoA by quartiles", labels = c("1st quartile", "2nd quartile", "3rd quartile", "4th quartile")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank()) +
facet_wrap(~ variable, scales = "free")
RoE_RW <- data_stata_win %>%
mutate(TA = log(TA),
LoanstoTA = LoanstoTA*100,
NonIntInc_pct = NonIntInc_pct*100,
T1LeverageRatio = T1LeverageRatio*100,
LoanLossProv_pct = LoanLossProv_pct*100,
NonPerfLoans_pct = NonPerfLoans_pct*100,
LongTermDebt_pct = LongTermDebt_pct*100,
LtD = LtD*100,
LeverageRatio = LeverageRatio*100,
RoE_1 = if_else(RoE <= quantile(RoE, 0.25), 1, 0),
RoE_2 = if_else(RoE > quantile(RoE, 0.25) & RoE <= quantile(RoE, 0.50), 1, 0),
RoE_3 = if_else(RoE > quantile(RoE, 0.50) & RoE <= quantile(RoE, 0.75), 1, 0),
RoE_4 = if_else(RoE > quantile(RoE, 0.75) & RoE <= quantile(RoE, 1), 1, 0),
RoE_quartiles = if_else(RoE <= quantile(RoE, 0.25), 1, if_else(RoE <= quantile(RoE, 0.50), 2, if_else(RoE <= quantile(RoE, 0.75), 3, if_else(RoE <= quantile(RoE, 1), 4, 0)))),
RoE_top_10 = if_else(RoE > quantile(RoE, 0.90), 1, 0),
RoE_bottom_10 = if_else(RoE < quantile(RoE, 0.1), 1, 0),
RoE_top_50 = if_else(RoE > quantile(RoE, 0.5), 1, 0)) %>%
gather(key = "variable", value = "value", est_RW) %>%
filter(variable != "Inflation" & variable != "GDP_pct") %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_quartiles))) +
geom_boxplot(outlier.colour = "grey") +
scale_fill_discrete(name = "RoE by quartiles", labels = c("1st quartile", "2nd quartile", "3rd quartile", "4th quartile")) +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank(),
axis.title.y = element_blank()) +
facet_wrap(~ variable, scales = "free")
grid.arrange(RoE_RW, RoA_RW, ncol=2)## Warning: Removed 61 rows containing non-finite values (stat_boxplot).
## Warning: Removed 61 rows containing non-finite values (stat_boxplot).
data_stata_z_gather %>%
filter(variable != "Inflation" & variable != "GDP_pct" & variable != "T1LeverageRatio") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(11, 10, 13, 3, 12, 4, 9, 7, 5, 6, 8, 1, 2)])) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_top_10))) +
geom_boxplot() +
theme(axis.text.x = element_text(angle = 90, vjust = 0.5),
axis.title.x = element_blank()) +
labs(y = "z-score", fill = "RoE in top 10%")## Warning: Removed 248 rows containing non-finite values (stat_boxplot).
data_stata_z_gather %>%
filter(variable != "Inflation" & variable != "GDP_pct" & variable != "T1LeverageRatio") %>%
mutate(variable = as.factor(variable),
variable = factor(variable, levels(variable)[c(11, 10, 13, 3, 12, 4, 9, 7, 5, 6, 8, 1, 2)])) %>%
ggplot(aes(y = value, x = variable, fill = factor(RoE_bottom_10))) +
geom_boxplot() +
theme(axis.text.x = element_blank(),
axis.title.x = element_blank(),
axis.ticks.x = element_blank()) +
labs(y = "z-score", fill = "RoE in bottom 10%") +
facet_wrap(~variable, scales ="free_x")## Warning: Removed 248 rows containing non-finite values (stat_boxplot).
data_stata_z_gather %>%
mutate(RoE_top_50 = factor(RoE_top_50)) %>%
select(id, Year, variable, value, RoE_top_50) %>%
spread(key = variable, value = value) %>%
select(1:5, 8:14, 17:18, 15:16) %>%
ggparcoord(alpha = .1,
scale = "center",
columns = 4:16,
groupColumn = "RoE_top_50")data_stata_z_gather %>%
filter(Year == 2010) %>%
mutate(RoE_4 = factor(RoE_4)) %>%
select(id, Year, variable, value, RoE_4) %>%
spread(key = variable, value = value) %>%
select(1:5, 8:14, 17, 15:16) %>%
ggparcoord(alpha = .3,
scale = "center",
columns = 4:15,
groupColumn = "RoE_4")Original
data_select %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LtD)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)vs Winsorised
data_stata_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LtD)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)data_stata_z_year_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LtD)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)Original
data_select %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = Efficiency)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)vs Winsorised
data_stata_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = Efficiency)) +
geom_jitter(width = 0.5, height = 0.5) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)vs Z-score by year winsorised
data_stata_z_year_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = Efficiency)) +
geom_jitter(width = 0.5, height = 0.5) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)Original
data_select %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LeverageRatio)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)vs Winsorised
data_stata_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LeverageRatio)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)vs Z-score by year winsorised
data_stata_z_year_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LeverageRatio)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)Original
data_select %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LoanLossProv_pct)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)vs Winsorised
data_stata_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LoanLossProv_pct)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)data_stata_z_year_win %>%
#filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = LoanLossProv_pct)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)data_select %>%
filter(Year == 2010) %>%
ggplot(aes(y = RoE, x = NonPerfLoans_pct)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)## Warning: Removed 21 rows containing non-finite values (stat_smooth).
## Warning: Removed 21 rows containing missing values (geom_point).
data_select %>%
filter(Year == 2018) %>%
ggplot(aes(y = RoE, x = NonIntInc_pct)) +
geom_point() +
scale_x_continuous(labels = percent_format()) +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)data_stata_win %>%
ggplot(aes(y = RoE, x = NonIntInc_pct)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)data_stata_z_year_win %>%
ggplot(aes(y = RoE, x = NonIntInc_pct)) +
geom_point() +
stat_smooth(method = "lm", se = FALSE, linetype = "dashed") +
facet_wrap(~ Year)data_stata %>%
ggplot(aes(x = LeverageRatio)) +
geom_histogram(bins = 100, alpha = 0.5,) +
geom_histogram(data = data_stata_win, fill = "green", alpha = 0.2, bins = 100) +
geom_histogram(data = data_stata_z_win, fill = "red", alpha = 0.2, bins = 100) +
scale_x_continuous(labels = percent_format()) +
#geom_vline(xintercept = 1) +
#geom_vline(data = data_stata_win %>% group_by(Year) %>% summarise(median = median((T1_pct/100)/LeverageRatio, na.rm = TRUE)), aes(xintercept = median), linetype = "dashed", col = "red") +
labs()data_stata %>%
ggplot(aes(x = LoanLossProv_pct)) +
geom_histogram(bins = 100, alpha = 0.3) +
geom_histogram(data = data_stata_win, fill = "green", alpha = 0.3, bins = 100) +
geom_histogram(data = data_stata_z_win, fill = "red", alpha = 0.3, bins = 100) +
scale_x_continuous(labels = percent_format()) +
#geom_vline(xintercept = 1) +
#geom_vline(data = data_stata_win %>% group_by(Year) %>% summarise(median = median((T1_pct/100)/LeverageRatio, na.rm = TRUE)), aes(xintercept = median), linetype = "dashed", col = "red") +
labs() +
facet_wrap(~ Year)data_stata %>%
ggplot(aes(x = LtD)) +
geom_histogram(bins = 100, alpha = 0.5,) +
geom_histogram(data = data_stata_win, fill = "green", alpha = 0.2, bins = 100) +
geom_histogram(data = data_stata_z_win, fill = "red", alpha = 0.2, bins = 100) +
scale_x_continuous(labels = percent_format()) +
#geom_vline(xintercept = 1) +
#geom_vline(data = data_stata_win %>% group_by(Year) %>% summarise(median = median((T1_pct/100)/LeverageRatio, na.rm = TRUE)), aes(xintercept = median), linetype = "dashed", col = "red") +
labs()data_stata %>%
ggplot(aes(x = NonIntInc_pct)) +
geom_histogram(bins = 100, alpha = 0.5,) +
geom_histogram(data = data_stata_win, fill = "green", alpha = 0.2, bins = 100) +
geom_histogram(data = data_stata_z_win, fill = "red", alpha = 0.2, bins = 100) +
scale_x_continuous(labels = percent_format()) +
#geom_vline(xintercept = 1) +
#geom_vline(data = data_stata_win %>% group_by(Year) %>% summarise(median = median((T1_pct/100)/LeverageRatio, na.rm = TRUE)), aes(xintercept = median), linetype = "dashed", col = "red") +
labs()data_stata %>%
ggplot(aes(x = Efficiency)) +
geom_histogram(bins = 100, alpha = 0.5,) +
geom_histogram(data = data_stata_win, fill = "green", alpha = 0.2, bins = 100) +
geom_histogram(data = data_stata_z_win, fill = "red", alpha = 0.2, bins = 100) +
#geom_vline(xintercept = 1) +
#geom_vline(data = data_stata_win %>% group_by(Year) %>% summarise(median = median((T1_pct/100)/LeverageRatio, na.rm = TRUE)), aes(xintercept = median), linetype = "dashed", col = "red") +
labs()Looking at 14 “random” samples
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = LtD, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = LtD, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))-> very different for individual vs agreggate
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = TA, y = RoE, group = Bank, col = Bank)) +
geom_point() +
scale_x_log10() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = TA, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
scale_x_log10() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))-> similar for individual and aggregate
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 3) %>%
ggplot(aes(x = Efficiency, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))With increasing Efficiency the gap between real RoE and epxected RoE increases as Efficiency hasn’t been taking into consideration yet.
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 5) %>%
ggplot(aes(x = Efficiency, y = RoE- (40.61+(-0.564*log(TA)-0.0240*LtD+0.0788*NonIntInc_pct-0.262*LeverageRatio-3.942*LoanLossProv_pct+0.571*Inflation+0.721*GDP_pct)), group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2)data_stata_win_reg %>%
mutate(id = as.integer(id)) %>%
left_join(random, by = c("id")) %>%
filter(random == 4) %>%
ggplot(aes(y = RoE, x = -10+40.61+(-0.564*log(TA)-0.0240*LtD-0.256*Efficiency+0.0788*NonIntInc_pct-0.262*LeverageRatio-3.942*LoanLossProv_pct+0.571*Inflation+0.721*GDP_pct), group = Bank, col = Bank)) +
geom_point() +
geom_abline(intercept = 0, linetype = "dashed") +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2)data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = Efficiency, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))-> mixed impact some increasing, some falling
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 4) %>%
ggplot(aes(x = T1_pct, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random < 10) %>%
ggplot(aes(x = T1_pct, y = RoE, group = Bank, col = Bank)) +
geom_point(alpha = 0.5, size = 0.5) +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 1) +
scale_y_continuous(limits = c(0, 30)) +
facet_wrap(~ random) +
theme(legend.position = "none")data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = T1_pct, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))-> mixed impact some increasing, some falling
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = LeverageRatio, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 3) %>%
ggplot(aes(x = LoanLossProv_pct, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 3) %>%
ggplot(aes(x = LoanLossProv_pct, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 3) %>%
ggplot(aes(x = NonIntInc_pct, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))Each year the level is different, but relationship the same
data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = NonIntInc_pct, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = LongTermDebt_pct, y = RoE, group = Bank, col = Bank)) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))data_stata_win %>%
left_join(random, by = c("id")) %>%
filter(random == 1) %>%
ggplot(aes(x = LongTermDebt_pct, y = RoE, group = factor(Year), col = factor(Year))) +
geom_point() +
geom_smooth(method = "lm", linetype = "dashed", se = FALSE) +
geom_smooth(aes(group = 1), method = "lm", linetype = "dashed", se = FALSE, colour = "black", size = 2) +
scale_y_continuous(limits = c(0, 30))## Reordering variables and trying again:
## Subset selection object
## Call: regsubsets.formula(RoE ~ ., data = data_stata_win_sub %>% select(-RoA),
## nvmax = 12)
## 22 Variables (and intercept)
## Forced in Forced out
## TA FALSE FALSE
## LtD FALSE FALSE
## LoanstoTA FALSE FALSE
## NonIntInc_pct FALSE FALSE
## Efficiency FALSE FALSE
## LeverageRatio FALSE FALSE
## T1_pct FALSE FALSE
## LoanLossProv_pct FALSE FALSE
## NonPerfLoans_pct FALSE FALSE
## LongTermDebt_pct FALSE FALSE
## Inflation FALSE FALSE
## GDP_pct FALSE FALSE
## est_T1LR FALSE FALSE
## est_RW FALSE FALSE
## FED_rate FALSE FALSE
## TA_large FALSE FALSE
## TA_1 FALSE FALSE
## TA_2 FALSE FALSE
## TA_3 FALSE FALSE
## TA_4 FALSE FALSE
## TA_5 FALSE FALSE
## TA_small FALSE FALSE
## 1 subsets of each size up to 13
## Selection Algorithm: exhaustive
## TA LtD LoanstoTA NonIntInc_pct Efficiency LeverageRatio T1_pct
## 1 ( 1 ) " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " " " "*" "*" "*" " "
## 5 ( 1 ) " " " " " " "*" "*" "*" " "
## 6 ( 1 ) " " "*" " " "*" "*" "*" " "
## 7 ( 1 ) " " "*" " " "*" "*" "*" " "
## 8 ( 1 ) " " "*" " " "*" "*" "*" " "
## 9 ( 1 ) " " "*" " " "*" "*" "*" " "
## 10 ( 1 ) " " "*" " " "*" "*" "*" " "
## 11 ( 1 ) " " "*" " " "*" "*" "*" " "
## 12 ( 1 ) " " "*" " " "*" "*" "*" " "
## 13 ( 1 ) " " "*" " " "*" "*" "*" " "
## LoanLossProv_pct NonPerfLoans_pct LongTermDebt_pct Inflation GDP_pct
## 1 ( 1 ) "*" " " " " " " " "
## 2 ( 1 ) "*" " " " " " " " "
## 3 ( 1 ) "*" " " " " " " " "
## 4 ( 1 ) "*" " " " " " " " "
## 5 ( 1 ) "*" " " " " " " " "
## 6 ( 1 ) "*" " " " " " " " "
## 7 ( 1 ) "*" " " "*" " " " "
## 8 ( 1 ) "*" " " "*" " " " "
## 9 ( 1 ) "*" " " "*" " " " "
## 10 ( 1 ) "*" " " "*" "*" "*"
## 11 ( 1 ) "*" " " "*" "*" "*"
## 12 ( 1 ) "*" "*" "*" "*" "*"
## 13 ( 1 ) "*" "*" "*" "*" "*"
## est_T1LR est_RW FED_rate TA_large TA_small TA_1 TA_2 TA_3 TA_4 TA_5
## 1 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " "*" " " " " " " " " " " " " " "
## 6 ( 1 ) " " " " "*" " " " " " " " " " " " " " "
## 7 ( 1 ) " " " " "*" " " " " " " " " " " " " " "
## 8 ( 1 ) " " " " "*" " " " " " " " " " " " " "*"
## 9 ( 1 ) " " " " "*" " " " " " " " " " " "*" "*"
## 10 ( 1 ) " " "*" " " " " " " " " " " " " " " "*"
## 11 ( 1 ) " " "*" " " " " " " " " " " " " "*" "*"
## 12 ( 1 ) " " "*" " " " " " " " " " " " " "*" "*"
## 13 ( 1 ) " " "*" " " " " " " " " "*" " " "*" "*"
res.sum <- summary(subset_selection)
data.frame(
Adj.R2 = which.max(res.sum$adjr2),
CP = which.min(res.sum$cp),
BIC = which.min(res.sum$bic)
)subset_selection2 <- lm(RoE ~ TA + LtD + LoanstoTA + Efficiency + T1_pct + LeverageRatio + LoanLossProv_pct + NonPerfLoans_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_sub %>% select(-RoA))
ols_step_forward_p(subset_selection2)##
## Selection Summary
## ------------------------------------------------------------------------------------
## Variable Adj.
## Step Entered R-Square R-Square C(p) AIC RMSE
## ------------------------------------------------------------------------------------
## 1 LoanLossProv_pct 0.3205 0.3200 1521.8888 7418.0950 3.9123
## 2 Efficiency 0.5557 0.5550 537.6086 6854.3076 3.1649
## 3 NonIntInc_pct 0.6137 0.6128 296.1160 6669.8051 2.9520
## 4 LeverageRatio 0.6566 0.6555 118.3492 6515.1456 2.7845
## 5 LtD 0.6628 0.6615 94.3897 6492.9200 2.7603
## 6 Inflation 0.6679 0.6664 74.6645 6474.3016 2.7401
## 7 GDP_pct 0.6732 0.6714 54.7197 6455.1455 2.7194
## 8 LongTermDebt_pct 0.6761 0.6742 44.3069 6445.0284 2.7081
## 9 TA 0.6790 0.6768 34.3720 6435.2729 2.6972
## 10 NonPerfLoans_pct 0.6818 0.6791 6.8422 5802.4154 2.6680
## ------------------------------------------------------------------------------------
## Reordering variables and trying again:
## Subset selection object
## Call: regsubsets.formula(RoA ~ ., data = data_stata_win_sub %>% select(-RoE),
## nvmax = 12)
## 22 Variables (and intercept)
## Forced in Forced out
## TA FALSE FALSE
## LtD FALSE FALSE
## LoanstoTA FALSE FALSE
## NonIntInc_pct FALSE FALSE
## Efficiency FALSE FALSE
## LeverageRatio FALSE FALSE
## T1_pct FALSE FALSE
## LoanLossProv_pct FALSE FALSE
## NonPerfLoans_pct FALSE FALSE
## LongTermDebt_pct FALSE FALSE
## Inflation FALSE FALSE
## GDP_pct FALSE FALSE
## est_T1LR FALSE FALSE
## est_RW FALSE FALSE
## FED_rate FALSE FALSE
## TA_large FALSE FALSE
## TA_1 FALSE FALSE
## TA_2 FALSE FALSE
## TA_3 FALSE FALSE
## TA_4 FALSE FALSE
## TA_5 FALSE FALSE
## TA_small FALSE FALSE
## 1 subsets of each size up to 13
## Selection Algorithm: exhaustive
## TA LtD LoanstoTA NonIntInc_pct Efficiency LeverageRatio T1_pct
## 1 ( 1 ) " " " " " " " " "*" " " " "
## 2 ( 1 ) " " " " " " " " "*" " " " "
## 3 ( 1 ) " " " " " " "*" "*" " " " "
## 4 ( 1 ) " " " " " " "*" "*" " " " "
## 5 ( 1 ) " " "*" " " "*" "*" " " " "
## 6 ( 1 ) " " "*" " " "*" "*" " " " "
## 7 ( 1 ) " " " " "*" "*" "*" " " " "
## 8 ( 1 ) " " "*" " " "*" "*" " " " "
## 9 ( 1 ) " " "*" " " "*" "*" "*" " "
## 10 ( 1 ) " " "*" " " "*" "*" "*" " "
## 11 ( 1 ) " " "*" " " "*" "*" "*" " "
## 12 ( 1 ) " " "*" " " "*" "*" "*" " "
## 13 ( 1 ) " " "*" " " "*" "*" "*" " "
## LoanLossProv_pct NonPerfLoans_pct LongTermDebt_pct Inflation GDP_pct
## 1 ( 1 ) " " " " " " " " " "
## 2 ( 1 ) "*" " " " " " " " "
## 3 ( 1 ) "*" " " " " " " " "
## 4 ( 1 ) "*" " " " " " " " "
## 5 ( 1 ) "*" " " " " " " " "
## 6 ( 1 ) "*" " " " " " " " "
## 7 ( 1 ) "*" " " " " " " " "
## 8 ( 1 ) "*" " " "*" " " " "
## 9 ( 1 ) "*" " " "*" " " " "
## 10 ( 1 ) "*" " " "*" " " " "
## 11 ( 1 ) "*" " " "*" " " " "
## 12 ( 1 ) "*" " " "*" "*" "*"
## 13 ( 1 ) "*" " " "*" "*" "*"
## est_T1LR est_RW FED_rate TA_large TA_small TA_1 TA_2 TA_3 TA_4 TA_5
## 1 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 2 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 3 ( 1 ) " " " " " " " " " " " " " " " " " " " "
## 4 ( 1 ) " " " " "*" " " " " " " " " " " " " " "
## 5 ( 1 ) " " " " "*" " " " " " " " " " " " " " "
## 6 ( 1 ) " " "*" "*" " " " " " " " " " " " " " "
## 7 ( 1 ) " " "*" "*" " " " " " " " " " " " " "*"
## 8 ( 1 ) " " "*" "*" " " " " " " " " " " " " "*"
## 9 ( 1 ) " " "*" "*" " " " " " " " " " " " " "*"
## 10 ( 1 ) " " "*" "*" " " " " " " " " " " "*" "*"
## 11 ( 1 ) "*" "*" "*" " " " " " " " " " " "*" "*"
## 12 ( 1 ) " " "*" "*" " " " " " " " " " " "*" "*"
## 13 ( 1 ) "*" "*" "*" " " " " " " " " " " "*" "*"
res.sum_RoA <- summary(subset_selection_RoA)
data.frame(
Adj.R2 = which.max(res.sum$adjr2),
CP = which.min(res.sum$cp),
BIC = which.min(res.sum$bic)
)subset_selection2_RoA <- lm(RoA ~ TA + LtD + LoanstoTA + Efficiency + T1_pct + LeverageRatio + LoanLossProv_pct + NonPerfLoans_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_sub %>% select(-RoE))
ols_step_forward_p(subset_selection2_RoA)##
## Selection Summary
## ------------------------------------------------------------------------------------
## Variable Adj.
## Step Entered R-Square R-Square C(p) AIC RMSE
## ------------------------------------------------------------------------------------
## 1 Efficiency 0.3434 0.3429 1623.9666 1225.4920 0.3827
## 2 LoanLossProv_pct 0.6028 0.6022 459.6630 557.9198 0.2978
## 3 NonIntInc_pct 0.6589 0.6581 209.7312 357.3408 0.2761
## 4 LeverageRatio 0.6697 0.6687 163.0751 316.3997 0.2718
## 5 LtD 0.6764 0.6751 135.1215 291.2589 0.2691
## 6 T1_pct 0.6880 0.6866 105.8233 256.8532 0.2668
## 7 GDP_pct 0.6931 0.6914 85.9046 238.2452 0.2647
## 8 Inflation 0.6991 0.6972 61.3270 214.8002 0.2622
## 9 TA 0.7012 0.6990 54.4372 208.1741 0.2614
## 10 LongTermDebt_pct 0.7056 0.7032 37.3207 191.4243 0.2596
## 11 NonPerfLoans_pct 0.7001 0.6974 12.1277 158.1214 0.2569
## 12 LoanstoTA 0.7004 0.6974 13.0000 158.9819 0.2569
## ------------------------------------------------------------------------------------
fe_win_within <- plm(RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + FED_rate, data = data_stata_win_reg, model = "within")
summary(fe_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct + FED_rate, data = data_stata_win_reg, model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.43044 -0.84523 -0.10495 0.66719 28.06779
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.863974 0.319066 -2.7078 0.006871 **
## LtD -0.017875 0.016389 -1.0907 0.275648
## LoanstoTA -0.016532 0.024013 -0.6885 0.491290
## Efficiency -0.254759 0.011615 -21.9336 < 2.2e-16 ***
## LeverageRatio -0.264780 0.057800 -4.5810 5.123e-06 ***
## LoanLossProv_pct -3.920973 0.110767 -35.3984 < 2.2e-16 ***
## LongTermDebt_pct 0.030308 0.036380 0.8331 0.404963
## NonIntInc_pct 0.078732 0.018101 4.3496 1.483e-05 ***
## Inflation 0.416178 0.129309 3.2185 0.001324 **
## GDP_pct 0.508912 0.198876 2.5589 0.010624 *
## FED_rate 0.408264 0.239233 1.7066 0.088170 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6845
## R-Squared: 0.64102
## Adj. R-Squared: 0.59266
## F-statistic: 190.416 on 11 and 1173 DF, p-value: < 2.22e-16
fe_win_within <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg, model = "within")
summary(fe_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.51681 -0.85985 -0.10643 0.67884 28.00367
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.563780 0.256839 -2.1951 0.02835 *
## LtD -0.024043 0.010057 -2.3907 0.01697 *
## Efficiency -0.256342 0.011511 -22.2699 < 2.2e-16 ***
## LeverageRatio -0.261744 0.056829 -4.6058 4.556e-06 ***
## LoanLossProv_pct -3.942435 0.107882 -36.5441 < 2.2e-16 ***
## NonIntInc_pct 0.078808 0.018045 4.3673 1.368e-05 ***
## Inflation 0.570824 0.092623 6.1629 9.796e-10 ***
## GDP_pct 0.721446 0.151821 4.7519 2.263e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6867
## R-Squared: 0.63987
## Adj. R-Squared: 0.5924
## F-statistic: 261.182 on 8 and 1176 DF, p-value: < 2.22e-16
fe_win_within_size_buckets <- plm(RoE ~ TA_1 + TA_2 + TA_3 + TA_4 + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg, model = "within")
summary(fe_win_within_size_buckets)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA_1 + TA_2 + TA_3 + TA_4 + LtD + LoanstoTA +
## Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.475253 -0.855885 -0.093122 0.697793 27.910994
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA_1 0.9636649 0.7127204 1.3521 0.17661
## TA_2 1.0828930 0.6104854 1.7738 0.07635 .
## TA_3 0.7249439 0.5500023 1.3181 0.18774
## TA_4 0.2078394 0.4402462 0.4721 0.63694
## LtD -0.0219347 0.0163445 -1.3420 0.17985
## LoanstoTA -0.0088437 0.0233963 -0.3780 0.70550
## Efficiency -0.2561131 0.0115005 -22.2697 < 2.2e-16 ***
## LeverageRatio -0.2695593 0.0582314 -4.6291 4.082e-06 ***
## LoanLossProv_pct -3.9379616 0.1078832 -36.5021 < 2.2e-16 ***
## LongTermDebt_pct 0.0311262 0.0365473 0.8517 0.39457
## NonIntInc_pct 0.0795531 0.0181003 4.3951 1.208e-05 ***
## Inflation 0.5804500 0.0931711 6.2299 6.496e-10 ***
## GDP_pct 0.7078371 0.1514980 4.6723 3.324e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6859.7
## R-Squared: 0.64025
## Adj. R-Squared: 0.59109
## F-statistic: 160.309 on 13 and 1171 DF, p-value: < 2.22e-16
LR effect much larger than for small banks
fe_win_within_large <- plm(RoE ~ LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + est_RW, data = data_stata_win_reg_large, model = "within")
summary(fe_win_within_large)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct + est_RW, data = data_stata_win_reg_large, model = "within")
##
## Unbalanced Panel: n = 74, T = 6-9, N = 623
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -11.417314 -0.808043 -0.095962 0.684833 24.183388
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## LtD -0.011718 0.019833 -0.5908 0.554876
## LoanstoTA -0.046338 0.031174 -1.4864 0.137748
## Efficiency -0.258409 0.015779 -16.3768 < 2.2e-16 ***
## LeverageRatio -0.532049 0.077592 -6.8570 1.932e-11 ***
## LoanLossProv_pct -3.243787 0.138608 -23.4026 < 2.2e-16 ***
## LongTermDebt_pct -0.023720 0.050047 -0.4740 0.635720
## NonIntInc_pct 0.104693 0.025300 4.1381 4.063e-05 ***
## Inflation 0.591348 0.125586 4.7087 3.175e-06 ***
## GDP_pct 0.916899 0.197644 4.6391 4.397e-06 ***
## est_RW 0.036859 0.011628 3.1698 0.001612 **
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7676.6
## Residual Sum of Squares: 2610
## R-Squared: 0.66001
## Adj. R-Squared: 0.60766
## F-statistic: 104.635 on 10 and 539 DF, p-value: < 2.22e-16
fe_win_within_large <- plm(RoE ~ LtD + LoanstoTA + Efficiency + T1_pct + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + est_RW, data = data_stata_win_reg_large, model = "within")
summary(fe_win_within_large)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ LtD + LoanstoTA + Efficiency + T1_pct + LoanLossProv_pct +
## LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct +
## est_RW, data = data_stata_win_reg_large, model = "within")
##
## Unbalanced Panel: n = 74, T = 6-9, N = 623
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -11.734656 -0.907406 -0.079009 0.669354 23.441760
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## LtD -0.0414755 0.0198462 -2.0898 0.0371001 *
## LoanstoTA -0.0304981 0.0324790 -0.9390 0.3481470
## Efficiency -0.2485885 0.0162156 -15.3302 < 2.2e-16 ***
## T1_pct -0.2323297 0.0645489 -3.5993 0.0003485 ***
## LoanLossProv_pct -3.0914994 0.1419484 -21.7790 < 2.2e-16 ***
## LongTermDebt_pct -0.0042584 0.0514705 -0.0827 0.9340933
## NonIntInc_pct 0.1050562 0.0261057 4.0243 6.534e-05 ***
## Inflation 0.5781802 0.1294374 4.4669 9.676e-06 ***
## GDP_pct 0.7644222 0.2026875 3.7714 0.0001803 ***
## est_RW 0.0023124 0.0122047 0.1895 0.8497987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7676.6
## Residual Sum of Squares: 2771
## R-Squared: 0.63903
## Adj. R-Squared: 0.58344
## F-statistic: 95.4199 on 10 and 539 DF, p-value: < 2.22e-16
fe_win_within_small <- plm(RoE ~ LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + est_RW, data = data_stata_win_reg_small, model = "within")
summary(fe_win_within_small)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct + est_RW, data = data_stata_win_reg_small, model = "within")
##
## Unbalanced Panel: n = 74, T = 7-9, N = 648
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -16.84155 -0.91748 -0.11930 0.83314 17.60955
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## LtD -0.0176585 0.0320195 -0.5515 0.581514
## LoanstoTA -0.0160934 0.0448215 -0.3591 0.719689
## Efficiency -0.2625698 0.0177499 -14.7928 < 2.2e-16 ***
## LeverageRatio -0.1665510 0.0905967 -1.8384 0.066532 .
## LoanLossProv_pct -4.6379699 0.1715886 -27.0296 < 2.2e-16 ***
## LongTermDebt_pct 0.0354611 0.0548854 0.6461 0.518482
## NonIntInc_pct 0.0725226 0.0271240 2.6737 0.007718 **
## Inflation 0.5841923 0.1472116 3.9684 8.17e-05 ***
## GDP_pct 0.3618509 0.2298033 1.5746 0.115907
## est_RW -0.0034617 0.0169842 -0.2038 0.838569
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 10884
## Residual Sum of Squares: 3716.4
## R-Squared: 0.65856
## Adj. R-Squared: 0.60831
## F-statistic: 108.782 on 10 and 564 DF, p-value: < 2.22e-16
fe_win_within_small <- plm(RoE ~ LtD + LoanstoTA + Efficiency + T1_pct + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + est_RW, data = data_stata_win_reg_small, model = "within")
summary(fe_win_within_small)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ LtD + LoanstoTA + Efficiency + T1_pct + LoanLossProv_pct +
## LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct +
## est_RW, data = data_stata_win_reg_small, model = "within")
##
## Unbalanced Panel: n = 74, T = 7-9, N = 648
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -16.69793 -0.94223 -0.14905 0.86000 17.73944
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## LtD -0.02862755 0.03162570 -0.9052 0.3657464
## LoanstoTA 0.00442590 0.04439323 0.0997 0.9206197
## Efficiency -0.25982846 0.01776288 -14.6276 < 2.2e-16 ***
## T1_pct 0.00058093 0.06739566 0.0086 0.9931256
## LoanLossProv_pct -4.59854575 0.17267904 -26.6306 < 2.2e-16 ***
## LongTermDebt_pct 0.02686797 0.05542203 0.4848 0.6280145
## NonIntInc_pct 0.07300970 0.02741320 2.6633 0.0079588 **
## Inflation 0.53032583 0.14540675 3.6472 0.0002897 ***
## GDP_pct 0.29675953 0.22791943 1.3020 0.1934352
## est_RW -0.01255799 0.01692172 -0.7421 0.4583219
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 10884
## Residual Sum of Squares: 3738.7
## R-Squared: 0.65651
## Adj. R-Squared: 0.60596
## F-statistic: 107.798 on 10 and 564 DF, p-value: < 2.22e-16
fe_win_within_large_interaction <- plm(RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + TA_large*LeverageRatio, data = data_stata_win_reg, model = "within")
summary(fe_win_within_large_interaction)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct + TA_large * LeverageRatio, data = data_stata_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.09350 -0.85399 -0.12553 0.66617 27.56110
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.464867 0.259163 -1.7937 0.0731150 .
## LtD -0.017445 0.016229 -1.0749 0.2826182
## LoanstoTA -0.011377 0.023229 -0.4898 0.6243987
## Efficiency -0.255768 0.011488 -22.2639 < 2.2e-16 ***
## LeverageRatio -0.072562 0.077190 -0.9400 0.3473895
## LoanLossProv_pct -3.943659 0.108430 -36.3704 < 2.2e-16 ***
## LongTermDebt_pct 0.018758 0.036255 0.5174 0.6049811
## NonIntInc_pct 0.081797 0.018036 4.5351 6.347e-06 ***
## Inflation 0.557859 0.092273 6.0457 1.996e-09 ***
## GDP_pct 0.700591 0.151512 4.6240 4.182e-06 ***
## LeverageRatio:TA_large -0.405739 0.107124 -3.7876 0.0001599 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6779.1
## R-Squared: 0.64447
## Adj. R-Squared: 0.59659
## F-statistic: 193.304 on 11 and 1173 DF, p-value: < 2.22e-16
Just being large or just having a high leverage ratio increases RoE. But having both decreases RoE. Hard to interpret.
fe_win_within_TA_interaction <- plm(RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + TA*LeverageRatio, data = data_stata_win_reg, model = "within")
summary(fe_win_within_TA_interaction)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct + TA * LeverageRatio, data = data_stata_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -17.83132 -0.81095 -0.11800 0.68606 27.75257
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 1.9715075 0.5379694 3.6647 0.0002587 ***
## LtD -0.0129338 0.0161778 -0.7995 0.4241741
## LoanstoTA -0.0182714 0.0231628 -0.7888 0.4303738
## Efficiency -0.2551373 0.0114214 -22.3385 < 2.2e-16 ***
## LeverageRatio 4.4188913 0.8815117 5.0129 6.188e-07 ***
## LoanLossProv_pct -3.9507197 0.1077545 -36.6641 < 2.2e-16 ***
## LongTermDebt_pct 0.0048958 0.0362202 0.1352 0.8925024
## NonIntInc_pct 0.0837494 0.0179341 4.6698 3.362e-06 ***
## Inflation 0.5648251 0.0916815 6.1607 9.935e-10 ***
## GDP_pct 0.7190349 0.1504657 4.7787 1.987e-06 ***
## TA:LeverageRatio -0.2070409 0.0388614 -5.3277 1.192e-07 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6699.9
## R-Squared: 0.64863
## Adj. R-Squared: 0.6013
## F-statistic: 196.85 on 11 and 1173 DF, p-value: < 2.22e-16
fe_changes_within_TA_interaction <- plm(RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct + TA*LeverageRatio, data = data_stata_changes_reg, model = "within")
summary(fe_changes_within_TA_interaction)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct + TA * LeverageRatio, data = data_stata_changes_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 8, N = 1184
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -24.648002 -0.762168 0.051927 0.804613 31.998188
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.239929 0.989444 -0.2425 0.8084503
## LtD -0.049708 0.029172 -1.7040 0.0886884 .
## LoanstoTA -0.047099 0.046930 -1.0036 0.3158017
## Efficiency -0.208476 0.015983 -13.0437 < 2.2e-16 ***
## LeverageRatio -0.010777 0.121912 -0.0884 0.9295747
## LoanLossProv_pct -3.517741 0.175533 -20.0404 < 2.2e-16 ***
## LongTermDebt_pct 0.016481 0.055589 0.2965 0.7669285
## NonIntInc_pct 0.070511 0.025384 2.7778 0.0055726 **
## Inflation 0.439690 0.120285 3.6554 0.0002698 ***
## GDP_pct 0.803846 0.195011 4.1221 4.058e-05 ***
## TA:LeverageRatio 0.245636 0.403027 0.6095 0.5423425
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 16720
## Residual Sum of Squares: 10254
## R-Squared: 0.38672
## Adj. R-Squared: 0.29218
## F-statistic: 58.7578 on 11 and 1025 DF, p-value: < 2.22e-16
Not significant effect anymore
fe_changes_within <- plm(RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_changes_reg, model = "within")
summary(fe_changes_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct, data = data_stata_changes_reg, model = "within")
##
## Balanced Panel: n = 148, T = 8, N = 1184
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -24.45982 -0.75937 0.05584 0.79372 32.07902
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.0064587 0.9028143 0.0072 0.9942934
## LtD -0.0478119 0.0289963 -1.6489 0.0994753 .
## LoanstoTA -0.0487267 0.0468395 -1.0403 0.2984504
## Efficiency -0.2090892 0.0159462 -13.1122 < 2.2e-16 ***
## LeverageRatio 0.0364249 0.0941233 0.3870 0.6988432
## LoanLossProv_pct -3.5016835 0.1734912 -20.1836 < 2.2e-16 ***
## LongTermDebt_pct 0.0151989 0.0555326 0.2737 0.7843750
## NonIntInc_pct 0.0694600 0.0253174 2.7436 0.0061834 **
## Inflation 0.4438072 0.1200581 3.6966 0.0002301 ***
## GDP_pct 0.8016603 0.1949180 4.1128 4.222e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 16720
## Residual Sum of Squares: 10258
## R-Squared: 0.3865
## Adj. R-Squared: 0.29262
## F-statistic: 64.636 on 10 and 1026 DF, p-value: < 2.22e-16
Having fewer Loans to Deposits was better in “bad” times
fe_win_within_pre <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_pre, model = "within")
summary(fe_win_within_pre)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_pre,
## model = "within")
##
## Balanced Panel: n = 148, T = 5, N = 740
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -17.567451 -0.796309 -0.082795 0.760486 24.441989
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.726565 0.704036 -1.0320 0.302500
## LtD -0.046716 0.020127 -2.3210 0.020630 *
## Efficiency -0.332153 0.021137 -15.7144 < 2.2e-16 ***
## LeverageRatio -0.042468 0.108575 -0.3911 0.695838
## LoanLossProv_pct -4.013115 0.164004 -24.4696 < 2.2e-16 ***
## LongTermDebt_pct 0.095027 0.058677 1.6195 0.105883
## NonIntInc_pct 0.090423 0.032317 2.7980 0.005312 **
## Inflation -0.111602 0.264743 -0.4215 0.673512
## GDP_pct -0.566837 0.456230 -1.2424 0.214575
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 13469
## Residual Sum of Squares: 5002.6
## R-Squared: 0.62859
## Adj. R-Squared: 0.52921
## F-statistic: 109.632 on 9 and 583 DF, p-value: < 2.22e-16
Negative effect of LR only in recent “good” times
fe_win_within_post <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_post, model = "within")
summary(fe_win_within_post)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_post,
## model = "within")
##
## Balanced Panel: n = 148, T = 4, N = 592
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -5.036583 -0.620436 -0.050682 0.487804 5.752468
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.2822422 0.5310816 -0.5314 0.59538
## LtD 0.0009352 0.0147233 0.0635 0.94938
## Efficiency -0.1408133 0.0134144 -10.4971 < 2.2e-16 ***
## LeverageRatio -0.3088197 0.0742547 -4.1589 3.853e-05 ***
## LoanLossProv_pct -3.1586843 0.5101364 -6.1918 1.377e-09 ***
## LongTermDebt_pct 0.0415394 0.0426692 0.9735 0.33084
## NonIntInc_pct 0.0541944 0.0222955 2.4307 0.01547 *
## Inflation 0.7238512 0.0888424 8.1476 3.978e-15 ***
## GDP_pct 1.0017963 0.0914847 10.9504 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 1230.1
## Residual Sum of Squares: 555.49
## R-Squared: 0.54842
## Adj. R-Squared: 0.38648
## F-statistic: 58.6987 on 9 and 435 DF, p-value: < 2.22e-16
fe_win_within <- plm(RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg, model = "within")
summary(fe_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct, data = data_stata_win_reg, model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.48964 -0.85775 -0.10251 0.67267 28.03657
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.5471732 0.2597143 -2.1068 0.03534 *
## LtD -0.0211176 0.0162915 -1.2962 0.19515
## LoanstoTA -0.0066837 0.0233279 -0.2865 0.77454
## Efficiency -0.2570244 0.0115483 -22.2565 < 2.2e-16 ***
## LeverageRatio -0.2676272 0.0578230 -4.6284 4.095e-06 ***
## LoanLossProv_pct -3.9554255 0.1090003 -36.2882 < 2.2e-16 ***
## LongTermDebt_pct 0.0278314 0.0363805 0.7650 0.44442
## NonIntInc_pct 0.0776885 0.0181055 4.2909 1.926e-05 ***
## Inflation 0.5700913 0.0927392 6.1473 1.079e-09 ***
## GDP_pct 0.7276180 0.1522017 4.7806 1.969e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6862
## R-Squared: 0.64013
## Adj. R-Squared: 0.592
## F-statistic: 208.826 on 10 and 1174 DF, p-value: < 2.22e-16
fe_z_win_within <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.651577 -0.125536 -0.018194 0.097316 3.480607
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.11690602 0.03724173 -3.1391 0.0017368 **
## LtD -0.00072611 0.00030908 -2.3492 0.0189761 *
## Efficiency -0.03794052 0.00167286 -22.6801 < 2.2e-16 ***
## LeverageRatio -0.00063090 0.00018206 -3.4654 0.0005485 ***
## LoanLossProv_pct -0.00482294 0.00014595 -33.0444 < 2.2e-16 ***
## NonIntInc_pct 0.00102781 0.00029044 3.5388 0.0004176 ***
## Inflation 0.06402162 0.01054012 6.0741 1.681e-09 ***
## GDP_pct 0.05095983 0.01058385 4.8149 1.665e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 352.01
## Residual Sum of Squares: 140.04
## R-Squared: 0.60218
## Adj. R-Squared: 0.54975
## F-statistic: 222.513 on 8 and 1176 DF, p-value: < 2.22e-16
fe_z_win_within <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + LoanstoTA + LongTermDebt_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + LoanstoTA + LongTermDebt_pct + Inflation +
## GDP_pct, data = data_stata_z_win_reg, model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.642449 -0.126181 -0.017243 0.097592 3.479637
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.11418392 0.03762503 -3.0348 0.0024600 **
## LtD -0.00054399 0.00049008 -1.1100 0.2672268
## Efficiency -0.03801235 0.00168062 -22.6181 < 2.2e-16 ***
## LeverageRatio -0.00064975 0.00018487 -3.5146 0.0004572 ***
## LoanLossProv_pct -0.00484012 0.00014790 -32.7260 < 2.2e-16 ***
## NonIntInc_pct 0.00101213 0.00029143 3.4730 0.0005334 ***
## LoanstoTA -0.00019661 0.00038581 -0.5096 0.6104119
## LongTermDebt_pct 0.00011542 0.00028492 0.4051 0.6854899
## Inflation 0.06386681 0.01055551 6.0506 1.938e-09 ***
## GDP_pct 0.05144893 0.01061467 4.8470 1.422e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 352.01
## Residual Sum of Squares: 139.97
## R-Squared: 0.60236
## Adj. R-Squared: 0.54919
## F-statistic: 177.844 on 10 and 1174 DF, p-value: < 2.22e-16
fe_z_year_win_within <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_year_win_reg , model = "within")
summary(fe_z_year_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_year_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -36.44226 -0.89415 -0.12863 0.74830 25.85385
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -1.331815 0.293006 -4.5453 6.051e-06 ***
## LtD -0.020466 0.011003 -1.8600 0.06313 .
## Efficiency -0.279868 0.013041 -21.4613 < 2.2e-16 ***
## LeverageRatio -0.112365 0.065243 -1.7222 0.08529 .
## LoanLossProv_pct -4.557797 0.129874 -35.0939 < 2.2e-16 ***
## NonIntInc_pct 0.079791 0.019997 3.9902 7.009e-05 ***
## Inflation 0.626420 0.105718 5.9254 4.087e-09 ***
## GDP_pct 0.679359 0.173470 3.9163 9.509e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 23724
## Residual Sum of Squares: 8969.5
## R-Squared: 0.62193
## Adj. R-Squared: 0.57209
## F-statistic: 241.812 on 8 and 1176 DF, p-value: < 2.22e-16
on own mildly significant
fe_z_win_within <- plm(RoE ~ TA + LtD + Efficiency + est_RW + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + est_RW + LoanLossProv_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 6-9, N = 1271
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.570306 -0.126926 -0.022207 0.104576 3.482553
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.16780763 0.03878424 -4.3267 1.649e-05 ***
## LtD -0.00088192 0.00033647 -2.6211 0.0088842 **
## Efficiency -0.03825327 0.00178649 -21.4125 < 2.2e-16 ***
## est_RW 0.00258866 0.00135172 1.9151 0.0557383 .
## LoanLossProv_pct -0.00476807 0.00015438 -30.8856 < 2.2e-16 ***
## NonIntInc_pct 0.00106471 0.00030482 3.4929 0.0004966 ***
## Inflation 0.06272209 0.01112743 5.6367 2.195e-08 ***
## GDP_pct 0.04828068 0.01088943 4.4337 1.018e-05 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 341.79
## Residual Sum of Squares: 136.97
## R-Squared: 0.59926
## Adj. R-Squared: 0.54355
## F-statistic: 208.419 on 8 and 1115 DF, p-value: < 2.22e-16
with LR highly significant and positive.
fe_z_win_within <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + est_RW + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + est_RW +
## LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 6-9, N = 1271
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.626632 -0.123223 -0.019827 0.095260 3.402844
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.14512249 0.03895676 -3.7252 0.0002049 ***
## LtD -0.00088737 0.00033429 -2.6545 0.0080555 **
## Efficiency -0.03879748 0.00178021 -21.7938 < 2.2e-16 ***
## LeverageRatio -0.00074921 0.00018945 -3.9547 8.148e-05 ***
## est_RW 0.00350251 0.00136267 2.5703 0.0102893 *
## LoanLossProv_pct -0.00480721 0.00015369 -31.2778 < 2.2e-16 ***
## NonIntInc_pct 0.00110382 0.00030300 3.6429 0.0002820 ***
## Inflation 0.06764314 0.01112491 6.0803 1.645e-09 ***
## GDP_pct 0.05163314 0.01085180 4.7580 2.212e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 341.79
## Residual Sum of Squares: 135.07
## R-Squared: 0.60481
## Adj. R-Squared: 0.54947
## F-statistic: 189.432 on 9 and 1114 DF, p-value: < 2.22e-16
fe_win_lag_within <- plm(RoE ~ TA + LtD + Efficiency + LeverageRatio + NonPerfLoans_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_lag_reg, model = "within")
summary(fe_win_lag_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + Efficiency + LeverageRatio + NonPerfLoans_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_lag_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 2-8, N = 1059
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -23.8016018 -0.8495670 0.0074348 0.8405250 18.5498369
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.1820368 0.3873171 0.4700 0.6385
## LtD -0.0074041 0.0128390 -0.5767 0.5643
## Efficiency -0.0735837 0.0151392 -4.8605 1.381e-06 ***
## LeverageRatio -0.4212981 0.0741571 -5.6812 1.804e-08 ***
## NonPerfLoans_pct -0.4609852 0.0584556 -7.8861 8.974e-15 ***
## NonIntInc_pct 0.0199236 0.0231235 0.8616 0.3891
## Inflation 0.1432377 0.1062939 1.3476 0.1781
## GDP_pct 1.0462641 0.1815904 5.7617 1.142e-08 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 7541.2
## Residual Sum of Squares: 6052.2
## R-Squared: 0.19745
## Adj. R-Squared: 0.059689
## F-statistic: 27.77 on 8 and 903 DF, p-value: < 2.22e-16
Having fewer Loans to Deposits was better in “bad” times
fe_win_within_pre <- plm(RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_pre, model = "within")
summary(fe_win_within_pre)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_pre,
## model = "within")
##
## Balanced Panel: n = 148, T = 5, N = 740
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.5501467 -0.0717446 -0.0011165 0.0664116 1.8374819
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.0752881 0.0635784 1.1842 0.236826
## LtD -0.0034565 0.0018176 -1.9017 0.057706 .
## Efficiency -0.0331206 0.0019088 -17.3517 < 2.2e-16 ***
## LeverageRatio 0.0260569 0.0098050 2.6575 0.008088 **
## LoanLossProv_pct -0.3814612 0.0148105 -25.7562 < 2.2e-16 ***
## LongTermDebt_pct 0.0060285 0.0052989 1.1377 0.255718
## NonIntInc_pct 0.0069085 0.0029184 2.3672 0.018249 *
## Inflation -0.0152636 0.0239078 -0.6384 0.523440
## GDP_pct -0.0459865 0.0412001 -1.1162 0.264807
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 124.79
## Residual Sum of Squares: 40.796
## R-Squared: 0.67307
## Adj. R-Squared: 0.58559
## F-statistic: 133.361 on 9 and 583 DF, p-value: < 2.22e-16
Negative effect of LR only in recent “good” times
fe_win_within_post <- plm(RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_post, model = "within")
summary(fe_win_within_post)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg_post,
## model = "within")
##
## Balanced Panel: n = 148, T = 4, N = 592
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -0.34558099 -0.05401360 0.00017307 0.04762960 0.69909600
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.00659729 0.04957027 -0.1331 0.894184
## LtD 0.00130083 0.00137425 0.9466 0.344379
## Efficiency -0.01503268 0.00125208 -12.0062 < 2.2e-16 ***
## LeverageRatio 0.01970112 0.00693081 2.8425 0.004686 **
## LoanLossProv_pct -0.41837253 0.04761528 -8.7865 < 2.2e-16 ***
## LongTermDebt_pct 0.00051583 0.00398267 0.1295 0.897007
## NonIntInc_pct 0.00381221 0.00208103 1.8319 0.067651 .
## Inflation 0.07009467 0.00829240 8.4529 4.328e-16 ***
## GDP_pct 0.10817136 0.00853902 12.6679 < 2.2e-16 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 14.887
## Residual Sum of Squares: 4.8395
## R-Squared: 0.67492
## Adj. R-Squared: 0.55834
## F-statistic: 100.348 on 9 and 435 DF, p-value: < 2.22e-16
fe_win_within <- plm(RoA ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg, model = "within")
summary(fe_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct, data = data_stata_win_reg, model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.0042407 -0.0791299 -0.0034487 0.0628206 2.2057718
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.02479769 0.02388842 1.0381 0.2994544
## LtD -0.00511315 0.00149848 -3.4122 0.0006663 ***
## LoanstoTA 0.00568357 0.00214569 2.6488 0.0081854 **
## Efficiency -0.02551588 0.00106221 -24.0215 < 2.2e-16 ***
## LeverageRatio 0.01912214 0.00531854 3.5954 0.0003374 ***
## LoanLossProv_pct -0.37293305 0.01002580 -37.1973 < 2.2e-16 ***
## LongTermDebt_pct 0.00024247 0.00334626 0.0725 0.9422480
## NonIntInc_pct 0.00733575 0.00166534 4.4050 1.155e-05 ***
## Inflation 0.05285569 0.00853012 6.1964 7.983e-10 ***
## GDP_pct 0.08137045 0.01399946 5.8124 7.927e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 191.41
## Residual Sum of Squares: 58.054
## R-Squared: 0.6967
## Adj. R-Squared: 0.65614
## F-statistic: 269.671 on 10 and 1174 DF, p-value: < 2.22e-16
fe_z_win_within <- plm(RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.864115 -0.145569 -0.011503 0.115404 3.545609
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.01984503 0.04238683 0.4682 0.6397365
## LtD -0.00051789 0.00035178 -1.4722 0.1412334
## Efficiency -0.04525092 0.00190397 -23.7666 < 2.2e-16 ***
## LeverageRatio 0.00086049 0.00020721 4.1528 3.522e-05 ***
## LoanLossProv_pct -0.00569352 0.00016612 -34.2741 < 2.2e-16 ***
## NonIntInc_pct 0.00121029 0.00033056 3.6613 0.0002621 ***
## Inflation 0.07616166 0.01199628 6.3488 3.093e-10 ***
## GDP_pct 0.07526315 0.01204605 6.2480 5.803e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 540.31
## Residual Sum of Squares: 181.4
## R-Squared: 0.66426
## Adj. R-Squared: 0.62001
## F-statistic: 290.838 on 8 and 1176 DF, p-value: < 2.22e-16
fe_z_win_within <- plm(RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + LoanstoTA + LongTermDebt_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + LoanstoTA + LongTermDebt_pct + Inflation +
## GDP_pct, data = data_stata_z_win_reg, model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.7266267 -0.1417871 -0.0063946 0.1120501 3.5514461
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.00538748 0.04271457 0.1261 0.8996526
## LtD -0.00154681 0.00055637 -2.7802 0.0055199 **
## Efficiency -0.04515985 0.00190795 -23.6693 < 2.2e-16 ***
## LeverageRatio 0.00095110 0.00020988 4.5316 6.453e-06 ***
## LoanLossProv_pct -0.00563297 0.00016791 -33.5486 < 2.2e-16 ***
## NonIntInc_pct 0.00127049 0.00033085 3.8401 0.0001295 ***
## LoanstoTA 0.00106494 0.00043799 2.4314 0.0151891 *
## LongTermDebt_pct -0.00007108 0.00032346 -0.2197 0.8261050
## Inflation 0.07721902 0.01198336 6.4439 1.696e-10 ***
## GDP_pct 0.07319717 0.01205052 6.0742 1.681e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 540.31
## Residual Sum of Squares: 180.4
## R-Squared: 0.66611
## Adj. R-Squared: 0.62146
## F-statistic: 234.216 on 10 and 1174 DF, p-value: < 2.22e-16
fe_z_year_win_within <- plm(RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_year_win_reg , model = "within")
summary(fe_z_year_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + LeverageRatio + LoanLossProv_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_year_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -1.6081497 -0.0795517 -0.0060333 0.0611544 1.8874881
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.00343535 0.02363688 0.1453 0.884468
## LtD -0.00109255 0.00088762 -1.2309 0.218614
## Efficiency -0.02603599 0.00105199 -24.7493 < 2.2e-16 ***
## LeverageRatio 0.01961562 0.00526321 3.7269 0.000203 ***
## LoanLossProv_pct -0.39358521 0.01047700 -37.5666 < 2.2e-16 ***
## NonIntInc_pct 0.00766031 0.00161313 4.7487 2.299e-06 ***
## Inflation 0.05515255 0.00852828 6.4670 1.462e-10 ***
## GDP_pct 0.08333564 0.01399387 5.9552 3.427e-09 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 194.95
## Residual Sum of Squares: 58.371
## R-Squared: 0.70058
## Adj. R-Squared: 0.66112
## F-statistic: 343.951 on 8 and 1176 DF, p-value: < 2.22e-16
Both LR and RW have positive impact on RoA. Increasing LR positive even if controlling for extra risk taking vie RW.
fe_z_win_within <- plm(RoA ~ TA + LtD + Efficiency + LeverageRatio + est_RW + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + LeverageRatio + est_RW +
## LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 6-9, N = 1271
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.770200 -0.142200 -0.014167 0.119111 3.521284
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.01354695 0.04392752 -0.3084 0.7578408
## LtD -0.00075593 0.00037694 -2.0054 0.0451588 *
## Efficiency -0.04575822 0.00200736 -22.7953 < 2.2e-16 ***
## LeverageRatio 0.00069018 0.00021362 3.2309 0.0012703 **
## est_RW 0.00492744 0.00153655 3.2068 0.0013803 **
## LoanLossProv_pct -0.00565317 0.00017330 -32.6198 < 2.2e-16 ***
## NonIntInc_pct 0.00131213 0.00034167 3.8404 0.0001298 ***
## Inflation 0.08018778 0.01254441 6.3923 2.398e-10 ***
## GDP_pct 0.07675745 0.01223646 6.2728 5.064e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 521.48
## Residual Sum of Squares: 171.74
## R-Squared: 0.67066
## Adj. R-Squared: 0.62454
## F-statistic: 252.057 on 9 and 1114 DF, p-value: < 2.22e-16
fe_z_win_within <- plm(RoA ~ TA + LtD + Efficiency + T1_pct + est_RW + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg, model = "within")
summary(fe_z_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoA ~ TA + LtD + Efficiency + T1_pct + est_RW +
## LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_z_win_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 6-9, N = 1271
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -2.749999 -0.148633 -0.014708 0.117186 3.508487
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA 0.02476368 0.04526536 0.5471 0.5844348
## LtD -0.00068310 0.00038221 -1.7872 0.0741721 .
## Efficiency -0.04601531 0.00201603 -22.8247 < 2.2e-16 ***
## T1_pct 0.03561315 0.02479684 1.4362 0.1512270
## est_RW 0.00638489 0.00157924 4.0430 5.640e-05 ***
## LoanLossProv_pct -0.00567189 0.00017401 -32.5949 < 2.2e-16 ***
## NonIntInc_pct 0.00131122 0.00034373 3.8147 0.0001438 ***
## Inflation 0.08328509 0.01255227 6.6351 5.052e-11 ***
## GDP_pct 0.07914706 0.01225442 6.4587 1.575e-10 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 521.48
## Residual Sum of Squares: 173.03
## R-Squared: 0.66819
## Adj. R-Squared: 0.62172
## F-statistic: 249.258 on 9 and 1114 DF, p-value: < 2.22e-16
fe_win_within_best <- plm(RoE ~ TA + LtD +LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + NonPerfLoans_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg, model = "within")
summary(fe_win_within_best)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + NonPerfLoans_pct + LongTermDebt_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 3-9, N = 1206
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.286153 -0.837518 -0.064745 0.651913 27.988909
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.561382 0.316541 -1.7735 0.0764381 .
## LtD -0.012702 0.017516 -0.7252 0.4685142
## LoanstoTA -0.034089 0.025222 -1.3515 0.1768168
## Efficiency -0.263574 0.013162 -20.0250 < 2.2e-16 ***
## LeverageRatio -0.324778 0.065160 -4.9843 7.272e-07 ***
## LoanLossProv_pct -3.871610 0.148436 -26.0827 < 2.2e-16 ***
## NonPerfLoans_pct -0.089726 0.063647 -1.4097 0.1589115
## LongTermDebt_pct 0.025053 0.037667 0.6651 0.5061264
## NonIntInc_pct 0.076201 0.020032 3.8040 0.0001506 ***
## Inflation 0.567120 0.100056 5.6681 1.866e-08 ***
## GDP_pct 0.782321 0.158989 4.9206 1.002e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 16827
## Residual Sum of Squares: 6165.5
## R-Squared: 0.6336
## Adj. R-Squared: 0.57831
## F-statistic: 164.597 on 11 and 1047 DF, p-value: < 2.22e-16
fe_win_within <- plm(RoE ~ TA + LtD +LoanstoTA + Efficiency + LeverageRatio + LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg, model = "within")
summary(fe_win_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + LeverageRatio +
## LoanLossProv_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_win_reg,
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.505555 -0.861255 -0.098293 0.684229 27.988835
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.544488 0.259645 -2.0971 0.0362 *
## LtD -0.017836 0.015714 -1.1351 0.2566
## LoanstoTA -0.011539 0.022444 -0.5141 0.6073
## Efficiency -0.256369 0.011514 -22.2650 < 2.2e-16 ***
## LeverageRatio -0.267146 0.057809 -4.6211 4.238e-06 ***
## LoanLossProv_pct -3.948910 0.108648 -36.3460 < 2.2e-16 ***
## NonIntInc_pct 0.078190 0.018090 4.3222 1.676e-05 ***
## Inflation 0.569084 0.092713 6.1381 1.140e-09 ***
## GDP_pct 0.726332 0.152166 4.7733 2.040e-06 ***
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 19068
## Residual Sum of Squares: 6865.4
## R-Squared: 0.63995
## Adj. R-Squared: 0.59214
## F-statistic: 232.046 on 9 and 1175 DF, p-value: < 2.22e-16
Removes macro factors and replaces with year dummy.
fe_win_twoways <- plm(RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + LtD, data = data_stata_win_reg, model = "within", effect = "twoways")
summary(fe_win_twoways)## Twoways effects Within Model
##
## Call:
## plm(formula = RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio +
## LoanLossProv_pct + LtD, data = data_stata_win_reg, effect = "twoways",
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.65604 -0.82396 -0.08100 0.65406 28.01129
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.156654 0.359553 -0.4357 0.66314
## Efficiency -0.254396 0.011536 -22.0526 < 2.2e-16 ***
## NonIntInc_pct 0.085760 0.018336 4.6771 3.248e-06 ***
## LeverageRatio -0.266556 0.056506 -4.7173 2.678e-06 ***
## LoanLossProv_pct -4.016691 0.136332 -29.4625 < 2.2e-16 ***
## LtD -0.020413 0.010072 -2.0267 0.04292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 14996
## Residual Sum of Squares: 6654.9
## R-Squared: 0.55623
## Adj. R-Squared: 0.49516
## F-statistic: 244.417 on 6 and 1170 DF, p-value: < 2.22e-16
fe_win_twoways <- plm(RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + LtD, data = data_stata_win_reg, model = "within", effect = "twoways")
summary(fe_win_twoways)## Twoways effects Within Model
##
## Call:
## plm(formula = RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio +
## LoanLossProv_pct + LtD, data = data_stata_win_reg, effect = "twoways",
## model = "within")
##
## Balanced Panel: n = 148, T = 9, N = 1332
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -18.65604 -0.82396 -0.08100 0.65406 28.01129
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -0.156654 0.359553 -0.4357 0.66314
## Efficiency -0.254396 0.011536 -22.0526 < 2.2e-16 ***
## NonIntInc_pct 0.085760 0.018336 4.6771 3.248e-06 ***
## LeverageRatio -0.266556 0.056506 -4.7173 2.678e-06 ***
## LoanLossProv_pct -4.016691 0.136332 -29.4625 < 2.2e-16 ***
## LtD -0.020413 0.010072 -2.0267 0.04292 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 14996
## Residual Sum of Squares: 6654.9
## R-Squared: 0.55623
## Adj. R-Squared: 0.49516
## F-statistic: 244.417 on 6 and 1170 DF, p-value: < 2.22e-16
##
## Lagrange Multiplier Test - two-ways effects (Honda) for balanced
## panels
##
## data: RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + ...
## normal = 19.631, p-value < 2.2e-16
## alternative hypothesis: significant effects
pooling <- plm(RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + LtD, data = data_stata_win_reg, model = "pooling")
pFtest(RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + LtD, data = data_stata_win_reg, effect = "twoways")##
## F test for twoways effects
##
## data: RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + ...
## F = 3.9041, df1 = 155, df2 = 1170, p-value < 2.2e-16
## alternative hypothesis: significant effects
##
## F test for twoways effects
##
## data: RoE ~ TA + Efficiency + NonIntInc_pct + LeverageRatio + LoanLossProv_pct + ...
## F = 3.9041, df1 = 155, df2 = 1170, p-value < 2.2e-16
## alternative hypothesis: significant effects
test2 <- read.csv("test_2.csv")
test2 <- lm(intercept ~ GDP_pct + Inflation, data = test2)
summary(test2)##
## Call:
## lm(formula = intercept ~ GDP_pct + Inflation, data = test2)
##
## Residuals:
## Min 1Q Median 3Q Max
## -2.69132 -0.09168 0.12044 0.59221 0.95964
##
## Coefficients:
## Estimate Std. Error t value Pr(>|t|)
## (Intercept) 1.0736 1.9809 0.542 0.607
## GDP_pct 0.3516 0.9158 0.384 0.714
## Inflation 0.6184 0.5613 1.102 0.313
##
## Residual standard error: 1.282 on 6 degrees of freedom
## Multiple R-squared: 0.1687, Adjusted R-squared: -0.1084
## F-statistic: 0.6087 on 2 and 6 DF, p-value: 0.5745
fe_within <- plm(RoE ~ TA + LtD +LoanstoTA + Efficiency + T1_pct + LoanLossProv_pct + + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_reg, model = "within")
summary(fe_within)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA + LtD + LoanstoTA + Efficiency + T1_pct +
## LoanLossProv_pct + +LongTermDebt_pct + NonIntInc_pct + Inflation +
## GDP_pct, data = data_stata_reg, model = "within")
##
## Unbalanced Panel: n = 148, T = 6-9, N = 1271
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -72.56524 -1.10040 -0.13567 0.93075 35.01315
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA -1.72996360 0.46117950 -3.7512 0.0001851 ***
## LtD 0.00096837 0.01301450 0.0744 0.9407002
## LoanstoTA -0.02138063 0.03197871 -0.6686 0.5038960
## Efficiency -0.32774991 0.01780462 -18.4081 < 2.2e-16 ***
## T1_pct 0.14271033 0.07686247 1.8567 0.0636183 .
## LoanLossProv_pct -5.36960432 0.17921708 -29.9615 < 2.2e-16 ***
## LongTermDebt_pct 0.02129951 0.05840697 0.3647 0.7154239
## NonIntInc_pct 0.08104736 0.03148046 2.5745 0.0101660 *
## Inflation 0.60097470 0.16757817 3.5862 0.0003500 ***
## GDP_pct 0.55368580 0.26850891 2.0621 0.0394322 *
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 46186
## Residual Sum of Squares: 19780
## R-Squared: 0.57174
## Adj. R-Squared: 0.51133
## F-statistic: 148.587 on 10 and 1113 DF, p-value: < 2.22e-16
fe_within_size_buckets <- plm(RoE ~ TA_1 + TA_2 + TA_3 + TA_4 + LtD + LoanstoTA + Efficiency + T1_pct + LoanLossProv_pct + LongTermDebt_pct + NonIntInc_pct + Inflation + GDP_pct, data = data_stata_reg, model = "within")
summary(fe_within_size_buckets)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = RoE ~ TA_1 + TA_2 + TA_3 + TA_4 + LtD + LoanstoTA +
## Efficiency + T1_pct + LoanLossProv_pct + LongTermDebt_pct +
## NonIntInc_pct + Inflation + GDP_pct, data = data_stata_reg,
## model = "within")
##
## Unbalanced Panel: n = 148, T = 6-9, N = 1271
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -72.66768 -1.10361 -0.14012 0.96900 34.73454
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## TA_1 3.22364905 1.28536752 2.5080 0.0122849 *
## TA_2 2.73147613 1.11601481 2.4475 0.0145382 *
## TA_3 2.55969027 0.99126413 2.5822 0.0099431 **
## TA_4 0.52452331 0.80919571 0.6482 0.5169875
## LtD 0.00051007 0.01305594 0.0391 0.9688433
## LoanstoTA -0.02798746 0.03215026 -0.8705 0.3842043
## Efficiency -0.32600873 0.01783580 -18.2783 < 2.2e-16 ***
## T1_pct 0.16941727 0.07664527 2.2104 0.0272803 *
## LoanLossProv_pct -5.30546719 0.17705426 -29.9652 < 2.2e-16 ***
## LongTermDebt_pct 0.02146024 0.05879640 0.3650 0.7151867
## NonIntInc_pct 0.08816540 0.03150390 2.7986 0.0052220 **
## Inflation 0.63351059 0.16858128 3.7579 0.0001803 ***
## GDP_pct 0.48108131 0.26659758 1.8045 0.0714204 .
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 46186
## Residual Sum of Squares: 19783
## R-Squared: 0.57167
## Adj. R-Squared: 0.50993
## F-statistic: 113.957 on 13 and 1110 DF, p-value: < 2.22e-16
fe_win_nowin <- plm(roe ~ ta + ltd +loanstota + efficiency + t1_pct + loanlossprov_pct + + longtermdebt_pct + nonintinc_pct + inflation + gdp_pct, data = residual_win_reg, model = "within")
summary(fe_win_nowin)## Oneway (individual) effect Within Model
##
## Call:
## plm(formula = roe ~ ta + ltd + loanstota + efficiency + t1_pct +
## loanlossprov_pct + +longtermdebt_pct + nonintinc_pct + inflation +
## gdp_pct, data = residual_win_reg, model = "within")
##
## Unbalanced Panel: n = 65, T = 1-8, N = 164
##
## Residuals:
## Min. 1st Qu. Median 3rd Qu. Max.
## -39.2697 -1.8292 0.0000 1.7771 37.9599
##
## Coefficients:
## Estimate Std. Error t-value Pr(>|t|)
## ta -2.1833e-11 3.1598e-11 -0.6910 0.49138
## ltd 8.8086e-03 7.1921e-02 0.1225 0.90280
## loanstota -7.3604e-02 1.8870e-01 -0.3901 0.69742
## efficiency -4.9418e-01 8.6576e-02 -5.7080 1.483e-07 ***
## t1_pct 7.1020e-01 4.2219e-01 1.6822 0.09604 .
## loanlossprov_pct -7.3518e+00 8.0792e-01 -9.0997 2.335e-14 ***
## longtermdebt_pct 5.8520e-02 6.0780e-01 0.0963 0.92351
## nonintinc_pct 2.5349e-01 2.2856e-01 1.1091 0.27038
## inflation 1.2324e+00 1.5728e+00 0.7836 0.43535
## gdp_pct 6.2590e-01 2.7406e+00 0.2284 0.81987
## ---
## Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
##
## Total Sum of Squares: 33677
## Residual Sum of Squares: 10637
## R-Squared: 0.68415
## Adj. R-Squared: 0.42153
## F-statistic: 19.2778 on 10 and 89 DF, p-value: < 2.22e-16